cifar10_3channel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e0d252f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1abd778dbf0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datagen inspo taken from \n",
    "# https://colab.research.google.com/github/reiinakano/invariant-risk-minimization/blob/master/invariant_risk_minimization_colored_mnist.ipynb#scrollTo=sopHPgEhu4Jo\n",
    "\n",
    "%matplotlib notebook\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torchvision import datasets\n",
    "import torchvision.datasets.utils as dataset_utils\n",
    "\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torchmetrics import Accuracy, AUROC\n",
    "import time\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import random\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "\n",
    "import pickle \n",
    "import seaborn as sns\n",
    "\n",
    "import scipy\n",
    "from skimage.util import random_noise\n",
    "\n",
    "SEED = 1234\n",
    "DEVICE = 'cpu'\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "106d2cbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "data_path = './data/'\n",
    "if not os.path.exists(data_path):\n",
    "    os.makedirs(data_path)\n",
    "train_mnist = datasets.CIFAR10(data_path, train=True, download=True)\n",
    "test_mnist = datasets.CIFAR10(data_path, train=False, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b80bf7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rescale_values(image,max_val=1.0,min_val=0.0):\n",
    "    '''\n",
    "    image - numpy array\n",
    "    max_val/min_val - float\n",
    "    '''\n",
    "    return (image-image.min())/(image.max()-image.min())*(max_val-min_val)+min_val\n",
    "\n",
    "# Convert MNIST PIL images to 3 channel NP arrays and return separated data and label arrays\n",
    "def preprocess_data(train_mnist, test_mnist):\n",
    "    data = np.zeros((len(train_mnist)+len(test_mnist),32,32,3))\n",
    "    labels = np.zeros(len(train_mnist)+len(test_mnist))\n",
    "    \n",
    "    for i, (img, label) in enumerate(train_mnist):\n",
    "        arr = rescale_values(np.array(img)).reshape((32,32,3))\n",
    "        \n",
    "#         im_arr = np.concatenate([np.array(arr),np.zeros((32,32,1)),np.array(arr)], axis=2)\n",
    "    \n",
    "        data[i] = arr\n",
    "        labels[i] = label\n",
    "        \n",
    "    for i, (img, label) in enumerate(test_mnist):\n",
    "        arr = rescale_values(np.array(img)).reshape((32,32,3))\n",
    "        \n",
    "#         im_arr = np.concatenate([arr,np.zeros((32,32,1)),arr], axis=2)\n",
    "\n",
    "        data[len(train_mnist)+i] = arr\n",
    "        labels[len(train_mnist)+i] = label\n",
    "    \n",
    "    return data, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5fb4bb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "data, labels = preprocess_data(train_mnist, test_mnist)\n",
    "print(data.max())\n",
    "print(data.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a48ec890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 32, 32, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d59a4112",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this number needs to be picked s.t. 50% is divisible by 10 and 9\n",
    "n_per_class = 2700\n",
    "\n",
    "sorted_data = np.zeros((n_per_class*10,32,32,3))\n",
    "sorted_labels = np.zeros((n_per_class*10,))\n",
    "\n",
    "ind = 0\n",
    "for i in range(10):\n",
    "    class_labels = np.where(labels==i)[0][:n_per_class]\n",
    "    sorted_data[ind:ind+n_per_class] = data[class_labels]\n",
    "    sorted_labels[ind:ind+n_per_class] = labels[class_labels]\n",
    "    \n",
    "    ind += n_per_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c7c428cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffled 60/20/20 split\n",
    "# split 60/40 into train and val+test, then split val and test by half\n",
    "data_splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=SEED)\n",
    "train_indices, val_indices = list(data_splitter.split(X=sorted_data, y=sorted_labels))[0]\n",
    "\n",
    "x_train = sorted_data[train_indices]\n",
    "y_train = sorted_labels[train_indices]\n",
    "x_val_test = sorted_data[val_indices]\n",
    "y_val_test = sorted_labels[val_indices]\n",
    "\n",
    "data_splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=SEED)\n",
    "val_indices, test_indices = list(data_splitter.split(X=x_val_test, y=y_val_test))[0]\n",
    "\n",
    "x_val = x_val_test[val_indices]\n",
    "y_val = y_val_test[val_indices]\n",
    "\n",
    "x_test = x_val_test[test_indices]\n",
    "y_test = y_val_test[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a76b327d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.5, 1.0, 0.0)\n",
      "(0.5, 0.9, 0.1)\n",
      "(0.5, 0.8, 0.2)\n",
      "(0.5, 0.7, 0.3)\n",
      "(0.5, 0.6, 0.4)\n",
      "(0.5, 0.5, 0.5)\n",
      "(0.5, 0.4, 0.6)\n",
      "(0.5, 0.3, 0.7)\n",
      "(0.5, 0.2, 0.8)\n",
      "(0.5, 0.1, 0.9)\n",
      "(0.5, 0.0, 1.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.5, 0.5, 0.5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_colours(neutral_scalar=0.5):\n",
    "    blue = 1.0\n",
    "    green = 0.0\n",
    "    colours = []\n",
    "    for i in range(11):\n",
    "        print((neutral_scalar,np.round(blue,1),np.round(green,1)))\n",
    "        colours.append((neutral_scalar,np.round(blue,1),np.round(green,1)))\n",
    "        blue -= 0.1\n",
    "        green += 0.1\n",
    "        \n",
    "    return colours\n",
    "\n",
    "colours = create_colours()\n",
    "colours.pop(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2628784",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8839dd42",
   "metadata": {},
   "outputs": [
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       "            buttonGroup = document.createElement('div');\n",
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       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '231.43764705882353')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# noise = random_noise(, mode='s&p')\n",
    "\n",
    "samp = data[0].copy()\n",
    "samp[:,:,0] *= 0.1\n",
    "samp[:,:,1] *= 0.5\n",
    "samp[:,:,2] *= 0.9\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1,4)\n",
    "axs[0].imshow(samp)\n",
    "axs[1].imshow(random_noise(samp[:,:,0], mode='s&p', amount=0.0))\n",
    "axs[1].set_title(np.sum(samp[:,:,0]))\n",
    "\n",
    "axs[2].imshow(samp[:,:,1])\n",
    "axs[2].set_title(np.sum(samp[:,:,1]))\n",
    "\n",
    "axs[3].imshow(random_noise(samp[:,:,2], mode='s&p', amount=0.0))\n",
    "axs[3].set_title(np.sum(samp[:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5eac4449",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create suppressor set where 50% of samples of all classes have colour c6=(0.5,0.5,0.5), \n",
    "# and the rest are split evenly 5% for each colour [c0,...,c10]\\{c6}\n",
    "\n",
    "lower = 0\n",
    "upper = 1\n",
    "mu = 0.5\n",
    "sigma = 0.3\n",
    "truncnorm = scipy.stats.truncnorm((lower-mu)/sigma,(upper-mu)/sigma,loc=mu,scale=sigma)\n",
    "\n",
    "def scale_to_sum(arr, arrsum):\n",
    "    return (arrsum / np.sum(np.abs(arr))) * arr\n",
    "\n",
    "def create_suppressor(data, colours, c6=(0.5,0.5,0.5), noise_scale=0.5):\n",
    "    num_samples = len(data)\n",
    "    suppressor_data = np.zeros(data.shape)\n",
    "    colour_labels = np.zeros((data.shape[0],3))\n",
    "    \n",
    "    c6_chunk = data[:int(num_samples/2)]\n",
    "    c6_chunk[:,:,:,0] *= c6[0]\n",
    "    c6_chunk[:,:,:,1] *= c6[1]\n",
    "    c6_chunk[:,:,:,2] *= c6[2]\n",
    "    \n",
    "#     noise = truncnorm.rvs(size=c6_chunk.shape)\n",
    "#     noise[:,:,:,1] *= 0\n",
    "    \n",
    "#     noise[:,:,:,0] = scale_to_sum(noise[:,:,:,0], np.sum(c6_chunk[:,:,:,0]))\n",
    "\n",
    "#     noise[:,:,:,1] = scale_to_sum(noise[:,:,:,1], np.sum(noise[:,:,:,0]*0.5))\n",
    "#     noise[:,:,:,2] = scale_to_sum(noise[:,:,:,2], np.sum(noise[:,:,:,0]*0.5))\n",
    "\n",
    "#     noise[:,:,:,1:] = scale_to_sum(noise[:,:,:,1:], np.sum(noise[:,:,:,0]))\n",
    "    \n",
    "#     c6_chunk = (1-noise_scale)*c6_chunk + abs(noise_scale*noise)\n",
    "\n",
    "    \n",
    "    c6_chunk = random_noise(c6_chunk, mode='s&p', amount=noise_scale, seed=SEED)\n",
    "    c6_chunk[:,:,:,1:] = scale_to_sum(c6_chunk[:,:,:,1:], np.sum(c6_chunk[:,:,:,0]))\n",
    "#     c6_chunk[:,:,:,1] = 0\n",
    "    \n",
    "    colour_labels[:int(num_samples/2)] = np.array([c6]*int(num_samples/2))\n",
    "    \n",
    "    suppressor_data[:int(num_samples/2)] =  c6_chunk\n",
    "    \n",
    "    for i, data_chunk in enumerate(np.split(data[int(num_samples/2):],10,axis=0)):\n",
    "        chunk_size = len(data_chunk)\n",
    "\n",
    "        data_chunk[:,:,:,0] *= colours[i][0]\n",
    "        data_chunk[:,:,:,1] *= colours[i][1]\n",
    "        data_chunk[:,:,:,2] *= colours[i][2]\n",
    "        \n",
    "#         noise = truncnorm.rvs(size=data_chunk.shape)\n",
    "#         noise[:,:,:,1] *= 0\n",
    "\n",
    "#         data_chunk = (1-noise_scale)*data_chunk + abs(noise_scale*noise)\n",
    "        data_chunk = random_noise(data_chunk, mode='s&p', amount=noise_scale, seed=SEED)\n",
    "        data_chunk[:,:,:,1:] = scale_to_sum(data_chunk[:,:,:,1:], np.sum(data_chunk[:,:,:,0]))\n",
    "#         data_chunk[:,:,:,1] = 0\n",
    "        \n",
    "        suppressor_data[int(num_samples/2)+chunk_size*i:int(num_samples/2)+chunk_size*(i+1)] = data_chunk\n",
    "        \n",
    "        colour_labels[int(num_samples/2)+chunk_size*i:int(num_samples/2)+chunk_size*(i+1)] = [colours[i]]*int(data_chunk.shape[0])\n",
    "    return suppressor_data, colour_labels\n",
    "\n",
    "\n",
    "# create confounder set where 50% of samples of all classes have colour c6=(0.5,0.5,0.5), \n",
    "# 25% have the class' colour and the rest are split evenly for each colour [c0,...,c10]\\{c6}\n",
    "def create_confounder(data, class_colour, colours,confounder_portion=0.25, c6=(0.5,0.5,0.5), noise_scale=0.5):\n",
    "    num_samples = len(data)\n",
    "    confounder_data = np.zeros(data.shape)\n",
    "    colour_labels = np.zeros((data.shape[0],3))\n",
    "\n",
    "    c6_chunk = data[:int(num_samples/2)]\n",
    "    c6_chunk[:,:,:,0] *= c6[0]\n",
    "    c6_chunk[:,:,:,1] *= c6[1]\n",
    "    c6_chunk[:,:,:,2] *= c6[2]\n",
    "    \n",
    "#     noise = truncnorm.rvs(size=c6_chunk.shape)\n",
    "#     noise[:,:,:,1] *= 0\n",
    "    \n",
    "#     c6_chunk = (1-noise_scale)*c6_chunk + abs(noise_scale*noise)\n",
    "\n",
    "    c6_chunk = random_noise(c6_chunk, mode='s&p', amount=noise_scale, seed=SEED)\n",
    "    c6_chunk[:,:,:,1:] = scale_to_sum(c6_chunk[:,:,:,1:], np.sum(c6_chunk[:,:,:,0]))\n",
    "#     c6_chunk[:,:,:,1] = 0\n",
    "    \n",
    "\n",
    "    colour_labels[:int(num_samples/2)] = np.array([c6]*int(num_samples/2))\n",
    "    confounder_data[:int(num_samples/2)] = c6_chunk\n",
    "    \n",
    "    n_confounded = int(num_samples/2)+int(num_samples*confounder_portion) \n",
    "    \n",
    "    confounder_data[int(num_samples/2):n_confounded] = data[int(num_samples/2):n_confounded]\n",
    "\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,0] *= class_colour[0]\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,1] *= class_colour[1]\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,2] *= class_colour[2]\n",
    "    \n",
    "#     noise = truncnorm.rvs(size=confounder_data[int(num_samples/2):n_confounded].shape)\n",
    "#     noise[:,:,:,1] *= 0\n",
    "    \n",
    "#     confounder_data[int(num_samples/2):n_confounded] = (1-noise_scale)*confounder_data[int(num_samples/2):n_confounded] + abs(noise_scale*noise)\n",
    "    confounder_data[int(num_samples/2):n_confounded] = random_noise(confounder_data[int(num_samples/2):n_confounded], mode='s&p', amount=noise_scale, seed=SEED)\n",
    "#     confounder_data[int(num_samples/2):n_confounded][:,:,:,1] = 0\n",
    "\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,1:] = scale_to_sum(confounder_data[int(num_samples/2):n_confounded][:,:,:,1:], np.sum(confounder_data[int(num_samples/2):n_confounded][:,:,:,0]))\n",
    "    \n",
    "    colour_labels[int(num_samples/2):n_confounded] = np.array([class_colour]*(n_confounded - int(num_samples/2)))\n",
    "    \n",
    "    other_colours = [x for x in colours if x != class_colour] \n",
    "    \n",
    "    for i, data_chunk in enumerate(np.split(data[n_confounded:],9,axis=0)):\n",
    "        chunk_size = len(data_chunk)\n",
    "\n",
    "        data_chunk[:,:,:,0] *= other_colours[i][0]\n",
    "        data_chunk[:,:,:,1] *= other_colours[i][1]\n",
    "        data_chunk[:,:,:,2] *= other_colours[i][2]\n",
    "        \n",
    "#         noise = truncnorm.rvs(size=data_chunk.shape)\n",
    "#         noise[:,:,:,1] *= 0\n",
    "        \n",
    "#         data_chunk = (1-noise_scale)*data_chunk + abs(noise_scale*noise)\n",
    "        data_chunk = random_noise(data_chunk, mode='s&p', amount=noise_scale, seed=SEED)\n",
    "        data_chunk[:,:,:,1:] = scale_to_sum(data_chunk[:,:,:,1:], np.sum(data_chunk[:,:,:,0]))\n",
    "#         data_chunk[:,:,:,1] = 0\n",
    "        \n",
    "        confounder_data[n_confounded+chunk_size*i:n_confounded+chunk_size*(i+1)] = data_chunk\n",
    "        \n",
    "        colour_labels[n_confounded+chunk_size*i:n_confounded+chunk_size*(i+1)] = [other_colours[i]]*int(data_chunk.shape[0])\n",
    "        \n",
    "    return confounder_data, colour_labels\n",
    "\n",
    "def create_no_col(data, c6=(0.5,0.5,0.5), noise_scale=0.5):\n",
    "    no_col_data = np.zeros((data.shape))\n",
    "    \n",
    "    c6_chunk = data\n",
    "    c6_chunk[:,:,:,0] *= c6[0]\n",
    "    c6_chunk[:,:,:,1] *= c6[1]\n",
    "    c6_chunk[:,:,:,2] *= c6[2]\n",
    "    \n",
    "#     noise = truncnorm.rvs(size=data.shape)\n",
    "#     noise[:,:,:,1] *= 0\n",
    "    \n",
    "#     c6_chunk = (1-noise_scale)*c6_chunk + abs(noise_scale*noise)\n",
    "    c6_chunk = random_noise(c6_chunk, mode='s&p', amount=noise_scale, seed=SEED)\n",
    "    c6_chunk[:,:,:,1:] = scale_to_sum(c6_chunk[:,:,:,1:], np.sum(c6_chunk[:,:,:,0]))\n",
    "#     c6_chunk[:,:,:,1] = 0\n",
    "    \n",
    "    return c6_chunk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "28264be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axs = plt.subplots(1,4)\n",
    "\n",
    "# for i in range(4):\n",
    "#     mask = x_train[i].copy()\n",
    "#     mask[mask != 0] = 1\n",
    "#     axs[i].imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aef555f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_to_bound(row, scale):\n",
    "    # scale = bound / np.max(np.abs(row))\n",
    "    return row * scale\n",
    "\n",
    "noise_scale = 0.0\n",
    "confounder_portion = 0.4\n",
    "c6_portion = 0.5\n",
    "\n",
    "conf_train = np.zeros(x_train.shape)\n",
    "conf_val = np.zeros(x_val.shape)\n",
    "conf_test = np.zeros(x_test.shape)\n",
    "\n",
    "sup_train = np.zeros(x_train.shape)\n",
    "sup_val = np.zeros(x_val.shape)\n",
    "sup_test = np.zeros(x_test.shape)\n",
    "\n",
    "no_col_train = np.zeros(x_train.shape)\n",
    "no_col_val = np.zeros(x_val.shape)\n",
    "no_col_test = np.zeros(x_test.shape)\n",
    "\n",
    "conf_train_colours = np.zeros((x_train.shape[0], 3))\n",
    "conf_val_colours = np.zeros((x_val.shape[0], 3))\n",
    "conf_test_colours = np.zeros((x_test.shape[0], 3))\n",
    "\n",
    "sup_train_colours = np.zeros((x_train.shape[0], 3))\n",
    "sup_val_colours = np.zeros((x_val.shape[0], 3))\n",
    "sup_test_colours = np.zeros((x_test.shape[0], 3))\n",
    "\n",
    "train_ind = 0\n",
    "val_ind = 0\n",
    "\n",
    "for i in range(10):\n",
    "    train_inds = np.where(y_train==i)[0]\n",
    "    val_inds = np.where(y_val==i)[0]\n",
    "    test_inds = np.where(y_test==i)[0]  \n",
    "\n",
    "    conf_train[train_inds], conf_train_colours[train_inds] = create_confounder(x_train[train_inds].copy(), colours[i], colours, confounder_portion=confounder_portion, noise_scale=noise_scale)\n",
    "    conf_val[val_inds], conf_val_colours[val_inds] = create_confounder(x_val[val_inds].copy(), colours[i], colours, confounder_portion=confounder_portion, noise_scale=noise_scale)\n",
    "    conf_test[test_inds], conf_test_colours[test_inds] = create_confounder(x_test[test_inds].copy(), colours[i], colours, confounder_portion=confounder_portion, noise_scale=noise_scale)\n",
    "    \n",
    "    sup_train[train_inds], sup_train_colours[train_inds] = create_suppressor(x_train[train_inds].copy(), colours, noise_scale=noise_scale)\n",
    "    sup_val[val_inds], sup_val_colours[val_inds] = create_suppressor(x_val[val_inds].copy(), colours, noise_scale=noise_scale)\n",
    "    sup_test[test_inds], sup_test_colours[test_inds] = create_suppressor(x_test[test_inds].copy(), colours, noise_scale=noise_scale)  \n",
    "\n",
    "    no_col_train[train_inds] = create_no_col(x_train[train_inds].copy(), noise_scale=noise_scale)\n",
    "    no_col_val[val_inds] = create_no_col(x_val[val_inds].copy(), noise_scale=noise_scale)\n",
    "    no_col_test[test_inds] = create_no_col(x_test[test_inds].copy(), noise_scale=noise_scale)\n",
    "\n",
    "#     no_col_train[train_inds] = (1.0-noise_scale)*(x_train[train_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_train[train_inds].shape))\n",
    "#     no_col_val[val_inds] = (1.0-noise_scale)*(x_val[val_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_val[val_inds].shape))\n",
    "#     no_col_test[test_inds] = (1.0-noise_scale)*(x_test[test_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_test[test_inds].shape))\n",
    "\n",
    "\n",
    "scale = 1 / np.max(np.abs(np.vstack([conf_train, conf_val, conf_test])))\n",
    "\n",
    "conf_train = np.apply_along_axis(scale_to_bound, 1, conf_train, scale)\n",
    "conf_val = np.apply_along_axis(scale_to_bound, 1, conf_val, scale) \n",
    "conf_test = np.apply_along_axis(scale_to_bound, 1, conf_test, scale) \n",
    "\n",
    "##############\n",
    "\n",
    "scale = 1 / np.max(np.abs(np.vstack([sup_train, sup_val, sup_test])))\n",
    "\n",
    "sup_train = np.apply_along_axis(scale_to_bound, 1, sup_train, scale)\n",
    "sup_val = np.apply_along_axis(scale_to_bound, 1, sup_val, scale) \n",
    "sup_test = np.apply_along_axis(scale_to_bound, 1, sup_test, scale) \n",
    "\n",
    "##############\n",
    "\n",
    "scale = 1 / np.max(np.abs(np.vstack([no_col_train, no_col_val, no_col_test])))\n",
    "\n",
    "no_col_train = np.apply_along_axis(scale_to_bound, 1, no_col_train, scale)\n",
    "no_col_val = np.apply_along_axis(scale_to_bound, 1, no_col_val, scale) \n",
    "no_col_test = np.apply_along_axis(scale_to_bound, 1, no_col_test, scale) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f3366c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5769154.264880513 5769154.264880525\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(conf_train[:,:,:,0]), np.sum(conf_train[:,:,:,1]) + np.sum(conf_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dacfd9c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5701626.027115809 5701626.027115811\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(sup_train[:,:,:,0]), np.sum(sup_train[:,:,:,1]) + np.sum(sup_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1557de36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8261780.348660242 8261780.3486602325\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(no_col_train[:,:,:,0]), np.sum(no_col_train[:,:,:,1]) + np.sum(no_col_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad2cdc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(no_col_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b38868bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(no_col_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a0ef3380",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_class_data(x_conf, x_sup, x_no_col, y, class_ind=0, n_cols=10):\n",
    "    class_inds = np.where(y==class_ind)[0]\n",
    "    \n",
    "    sample = random.sample(list(class_inds), n_cols)\n",
    "    fig, axs = plt.subplots(3,n_cols, figsize=(10,4))\n",
    "    \n",
    "    dataset_labels = ['Confounder', 'Suppressor', 'No\\nColour']\n",
    "    \n",
    "    for i, dataset in enumerate([x_conf, x_sup, x_no_col]):\n",
    "        for j in range(n_cols):\n",
    "            if i == 0:\n",
    "                axs[i,j].set_title(f'Sample\\n{sample[j]}')\n",
    "            axs[i,j].imshow(dataset[sample[j]])\n",
    "            axs[i,j].set_xticks([])\n",
    "            axs[i,j].set_yticks([])\n",
    "            \n",
    "        axs[i,0].set_ylabel(dataset_labels[i])\n",
    "            \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ed80b7c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "window.mpl = {};\n",
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       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
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       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
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       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
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       "\n",
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       "\n",
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       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
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       "            'resize: both;'\n",
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       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
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       "        1;\n",
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       "        'style',\n",
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       "                    'width',\n",
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       "                );\n",
       "                canvas.setAttribute(\n",
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       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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ItxatcFgB0PWw6bFeIL8/9d//Vf61Kc+xecPt29+85v0H//xH3TffffRkiVL6I1vfCPPd+gh27YMBIygbxnvcbM/xS9/+Uv6+te/Tvvvvz8LDSi8//mf/6E//OEP9J3vfIeVHLZvfOMbbMSDjENRFQoFVug33ngj7/+6173O3yvI+Omnn07HHXccPfvZz2bl+Ytf/IIuvPBCNhh0g8Ea3/7xj3/Q1772NTrjjDPYSdDJmxr8wAXPGW4vf/nLWbhigyEOA/SLX/xiwz7AOySDrb6Ldt9HJ2IIxQcDEgbXihUrCFg2I+itjhGQUryHAw88kM8JoxfKEKQR4/ELX/hCAwwgkp/97Gfp9a9/Pb3oRS/i/W+55RY+/m1ve1vHQNYqTngePOPTn/50Gh4eprvuumtSgo55/a53vYv+7d/+jeAkwly/4IILeM5+7GMf88+O+f32t7+dHRcg2TfccMOkBP1Xv/oVywaMdTjiYCzDyID8ABnohK3V+frhD3+YHZFvfetb6aCDDmJCDcweeugh+tOf/kT77bcfP0478/X4449n2br33nvTwoULGUeQ9mq1yjIUsqDTt1bHYqu6AiQLxjrGITAdHBxknHEdOC7hpA23uTBnW8UofC6MtZNPPplGR0cnEHSQZxByEE3INxjpkJNw5oKwP//5z+dTgaANDAwwkYB+hoMMTmIQDewDMh46KeFkg5wA7pdffjm95S1v6TiCjmeHPJs/fz7Lq0WLFvEzQS695jWvYbsCMhv7QIa/9KUvpXnz5tFvfvMbuuSSS9gxi+fXrR0dAd2NOfn//t//Y30CeQaHHXQVnJU9PT0dNV1bGXftjpFW9QSAaHVutnPO2QIYNgHk/GGHHcZjC4QapBn4XXPNNRyYascGw3E4BvMbegDOHsh8YLHnnntycCydTvM5oX9h+6pdAmc39CfsRIxr27YMBIygbxnvcVaeAkbjvvvuyyQcUQ5s8BzCcwyPuyr6oaEh2nbbbVl4QFlie/TRR5l4wTCHUMOGSN4LXvACNkqhSEKvdvwBjz32WI6qwBjebrvtZuX5W72oGvwXXXQRC9TJNpCWtWvXsjHR7tbsXbTzPtq93hO1PyJBiJovX76c4JSBg6MZQW92P+2MEWSBwBMO41/HHZQsIqFf+cpX6IMf/OAT9ciJrtMqTqtWrWLjFIbje97zHibHzSLo4+PjbCDAkP/5z3/u7wlk9H//9385mgYCiQ0RK2QWwPBHtB0Os8ki6DA0sC/epUbMP/3pT7PTAEbGHnvskej5N+VBrcxXZB4ARzgbMK91w3PDOAdJggNxsm2y+dpsfxhmiBTDcYcIaadvrY7FVnUFnv8pT3kKR851W7duHT31qU9lfYOoqW5zZc62ipE+1+rVq/lZ4RQCSY9H0JEBhGwO6A5ghQ2OHsynpUuXsnGPrVQq8dwLMzzwPZyQGrkLHR4PPvggE0/ocuAP/dVpEXTIDkQY8eyQL7oh2o9sAcgnzDfIvvDv2O+YY45hfYIIPCLqU23NdARkBfR2uOGauDYca9BBnbS1Mu7aGSPt6IlW52Y755xNbP/85z+zXM7n8/42MI7gXMU80aBBq3LuRz/6ETvAoG/DbFPMS8xPOHye+cxncrYp5iQc3BhrusGORnYbgg3xrNbZxMmunRwBI+jJsbMjiVgQaHoXAAGRgtAKjXp8v/XWWzP5/u///m/G7T//8z85rRgefkTedEP0HKlpiNY973nPa4oxlAyu84xnPINJVadvocGPSCTIUbN0XiXoiEDC6REapK08Y/xdtPM+Wjn/bO/TDkFvd4xAsYGswnutUQ9kcfz+97+nRx55hA1U/K3ddzIbmLWK01QEHVFfGAnIaHnFK17hHwORKRj3iKSDrMe3qQg6CDgMZOCMCIFuSCmFAw/peSDrs721Ml9hRCJ1FjJMHYy4b4wREHc4KZBpMNXWbL422x9kFJE6RE2mO+dsYxe//lRjsVVdMdkzIaKOdwV8dJuLc7aV+QoiCV0J/QkHUJygI4KH9HSUsISbznFkyuy2226TDg9EfnEOZMnFS1j0oE4l6HBafelLX6I1a9b4bDTcM74HTggQTJb2e+mll3KUvRVS00xHNAMUGQeQAR/60IfYudupWyvjLrz3ZmOkHT3R6txs55ydiC2CVtjUKdaqnEPmxbvf/W62p8PSHf0epS1wuKGsArIvrpsR3IEzDvbzD3/4w06Exu6pTQSMoLcJ2JN9dxigME4RaYRSgyGK2hcVCBDCF198MX31q19l8g6iiVRGeKmRaocUeWxIawcZhzILU+qQ4g1P9lSGAtLSkCrfiR7qZuNDDX4YOEhjQoQWKbEwHkJBDIIOryzwgBd7q622YpwQMUHUMb5N9y6wf6vvYy6M63YMiunGCMYw8MP7QN0/iBbqO5GyphuUHcgo+gegrgxEAMoW0RoYvp26tYrTVAT9tNNOY7KMqBPSYHXDuIQD4wMf+EBT43Mqgg4ZAVKP7BpN/9bzIlqP71AGM9tbq/MV2QWI2iEFUVPc4WSA0xBECUQq3FqZr7o/xhqifsgQQvQERAI12C95yUtmG562rj/VWJypbEKZCYxS9ECYy3N2uvmKsQS9iUwBOLqReRYn6IiaQzbFe5ig3wb2jZeNxV8iynyQ+o0oXrwHjO7bqQQdZQ6HHnooE230JUCtLvQosvPg2IAtMtkGGwL7YX+1TXTfVnREs/MiioqoKSL7KJvq1G26cdfKGGlHT7SqT9s5Z6dhi2w06DI4ojXVvFU5Bwc2ou/QK3DsIDMU5RbvfOc72U6ETYNNg1goRwl7GyFjBo4oyALNaO00fOx+2kPACHp7eD3p90aNFwxSbKiHAVFGap2muyIVDx48kHHdEP0BmQ8VIOrf4BEEIQ83FTLwfsdrsXU/pA8hQo+an05sxBIfJFD+qB9EJBJYQBCDyMBgx9+QtoQNdbxIXYKQxt/g6ABub3jDG7gRTXyb7l1g/1bfx1wY2O0YFNONEUQiQ+MJtYlwIkG5YkNaPWoZYewhGo80M7wb7AODEF5tKM5O3FrFaSqCjr/hGZHKHd9A2IEXDIX4NhVB17+BdCrOejzIORxXiNDP9tbqfEVTLjgnkXqoG0g5IkCaZhw+SyvzVfdHNBTjDhvGIMbfZJHN2cZrqutPNRZnIpuQYYWMLDiR4MCYy3N2Koxg8MNg32WXXZg8aw1xnKCDnAITzC2UmugGByPmFOYe0uMn2+D4gSMAKe2T6dROJeh4JjhQQYhBqnWDIxXfT7bB2Qjdi2OQYRDPaptOR0x2XqS1owwA9s1UWQuzPW9b1RN6n83GSKt6oh192uo5Zxu/ZtdHWjt606BnBpxD2NqRczgOPTfQz0Q3lEt873vf8+MT+gZR+njGGRwC6GmEeYrAl21zHwEj6HP/HT6hTwDPHFJ+kZaKzpSov0FzCkR7sSEiiVRMEEyQcAgKeLCRfgYDQuu8YOCjlgZkNdzQQRSG+vvf/37unB3fkK6Ga8FjjlSfubrBuEdKIRrzhB3a488D7z68/GG3d91nunfRzvuYCzi2alC0MkZgiCLSgXEJZw8ixSi70O6rqLEGIceGtFIQMWwYn3Cg4BrYpxO3VnGaiqDDWQQCDodZfAMuaEiFWvT4NhVBh0GBbJB4VB7nwDwApijv6MSt2XzFcyCDCOmskGdwGMKoR+o7ZJ02f2xnvuq+iMIj+whGPow+OOnmQv15/N1NNRZb1RXxc8LghYEK3YMmSlp2Mlfn7FQYwSGI7B5kCcCpNRlBR/NFOIChFxGBRCQN8gw/aGw5VfmI1nBjX6TYTrZ1MkHHHMEPUn/h0EL6L7BDJt5k2U6qW+Opwvr80+mIZjhpHTEyF5B238lbq3oCzzDZGGlVT7QzN1s9Z6dhC3vsOc95DkfPIf+1l007cg62IOxlzOUdd9yRz4MxDHsYulU3OO1Q8oK/IYoOPYG5C7scNkozx3qn4WX3Mz0CRtCnx8j2mAIBpMXB26dN4WAgwBONlEzd0KQFnmQ0n9FIcNIIOpQuPJOILkMZz+UNqYRwMoAETdYQD4YZ6o5aqc+Nvwtg0+r7mAs4tmpQJBkjMNagHIE3Uri1ngulBYiwhO9HGyppA6VOw65VnCyC3t6bC+crIpuIvqEsBSU8usHpAwMNTQWnM9Cbzddmd4QsI3T3RdS0k0srmt37VGMxiWyC4xcGKSKeSPnWTsm49lyds5NhBIcVHIbI1EHqNrbJCDr+hl4IcOIAI2y6jBjIIox+lKXEN+hjjGvoVETppto6laDDgYr7x5gIG8Zi+UYEEZBVEF+iCnMJuLSiVxWTuI6IYwUyhTmNzA44fTtl2cjJ3mmremKqMdJqtLududnqOduT3pt3bzhnUXIDZxia4YWrbbQq51Beh7GD48PSR8x9/KCcSvs1ockyggZakgf7BD0PUOICGyaMwG/eJ7ezb04EjKBvTnSfBOdGejsMCHgPQWaQiofvUDsdbljLEU0zEH3Hhr/D2wzvYjs16CD5UCyIXnV1dc1phLU+EPX8iMI12zTlHwY/0uSn2sJ3gRRbrI/Z6vuYC0C2alAkGSOo79X15vEJLzSiUEj3RKZHuGnTFkR7UbfeaVurOFkNentvLpyvwBhR82Z14RgTSDMOu4s3u1J8vk51N0hVhlOgE0oA2kFtsrGYRDYhJRl9TWCEIp0Txmy4zdU5OxlGyDYB6cY4QlYGNuhP9Dv45Cc/yToURCDsIg1yjrpVfIcmqkiZhX5uFiVG3Tkc5SCVqG+djlB2KkFH9g36NYT9Q4CV9iHBc4ad6ZF+DkIPXNpZ2jGuI8Kxh0wOOOvgFEH2y5bSTHS6MdJqvXg7c7PVc7YjhzbnvrDf8O7hCIKTJmx63I6cw3J/KA0F+Q6366+/njPWmvVcgkMYzgEEwNCDAvIATqp4s8jN+fx27s2HgBH0zYftk+LMWEoInnlE0KEkYUhC6aHeMtyQsoPaGQgTbOjkDIIQ7+KuKWLong1DJNxAlCB8jjrqKDY85vqGOmkYTjCqdH3L+DPBa4qU6lYazoTvAjW92nG7lfcxF7BshXgmHSNY/g9r2SJagHRibOiZgI6qcJKERjAMZ0ReoEg7cV3qVnDC801F0DEuYbzHDXvUZyNSgOVdmq0DP1WKO+Y6Ip6TdXFHZsJJJ53UsUMxnK8YJ+i1gdRi1P2FGww0ONwQCZlqi8/XqfZFtB416fGSoI4Fy93YZGOxXdkEAx94I3MKUVH0Pmm2zcU5OxlG0HPh+tzNnhfGO4j4ZBtkGaK5kItYJ1w36Gs4mOBMwvrmrazX3akEHc5o9MCJzzeME0QZwzkKOY/MO8h6/H0yvdsMz2Y6AvshwwUrzmDOw5mCZmhzYZtOT7QyRtrRE63OzXbOOds4owwJDi4EnzCP4o0G25FzCAzA0aM2sj4byDZS55vZceHz6yop6K0De9G2uY+AEfS5/w6fkCdA3V/YzRkXRToPamFQ/4K/IxUYXjx4tNFhUiPj8PpjzVooMShLbPgODZWarYMOryPSh+Np30jTQxoPvIxYU32ubKhzjittCGKs6Y30Jyh+pDMiIyDMCkDEDOmHIANQAPCiYmvlXcCYwnVbfR9zAcvpDAo8w3RjpNm7wHFosgRDFmmS2icBPRCQuRBmhEAhI4UZTbxAODtxawUn3Pd066DDGQaHW1iuAlKOsgzUFKKJXnybbh10yAGMcYxnnd8g5YiaAE/8fba3Vuarrk2OBj7hutBwQmJeQ65pdK7V+Yq6QfTs0IaboYGG9wCCGq57O9s4tXL9ycZiu7IJddiokUaDUmA72TYX5+xkGGEsISoXbhhLiPyCvCMrDen+IfEO94UzDboYtalhGQb0NZzf0A2I+MXH22TYdipBR1YFottYBkx7iOAZXvva13KTVcgqOFLh9AcJAomCHTJZBl47OkJTm6EXEMHfaaedWpkWHbHPVHqi1TECm69VPdHq3GznnLMJJAJScBSiKShsuHA5Ur2vduQcmoAiYwYZGIjI6wYbBNjBAQWi3myDAxM2DI7Fu9PQ9NJqAAAgAElEQVT+ObOJj1175ggYQZ85hk+KM0DZgURC4WPNYigmLJuE1HYsCQHijA1pd6hlg+EA4QWDE4YVPPgg7TheN00ZhcEFoxZNp+A9xXlhjMY31OXgPFC47Xi+Z/sFwZmACAWMbDg54OkE6UNJADysICVY2glkHD8giFBSSNGD0gc+2jlfDY9W3kW772O2cZrs+lBaqKlCAxSQHowr7XwPpRYaqNONEWR7AFNEPaHE0B8BS3shUo5zoemKbngHGJcg7WjSgv2x/jcMZ5BWOFc6aWsFJzi+8AzY4JBAlATZANjQlCaMimPeghghcgzDFsY8CCLINFJsdUOKnxIAYItafnSMRnkAfsK6aVwThgTkA5afQYYI7huNgTAnOmFrZb7iPhE5QQooZCP+DdkEHJCKDQKvndxblZ0Y4zB2EfWDEwglFiAd6KkAhxAMtE7uCh2+u1bGYqu6Qg17EKvjjz9+whABvrrO9Vyas61gFH/YyWrQMa8RLcfcAvGGswulOOhfgpIA7ewOfYyxhewfRNmgy8MNJVFhFBByDs5kbJATOFazF3AtNDqd7Q3EG3MWdeaQNfiEnAEJR0d1pAYDH2QLYG7CiRgvKcNz6LO0oyOQvQB8YMsg0y3c0My2E5dFnG7cwbZqZ4y0qifamZutnnM2xx7GCbKg4CDSrLvwfrCkaDs2GGrH0fwSgS3YItDHmLto1opxBCeUbrBH4BTC+EOgDJmniLQj46ZZZtts4mTXTo6AEfTk2D2pjkQjFqSVw2DEGr1Q+BAmECRQ1LohCgTDAPui8zE2kBxEycI1G/E9vH5opATyCeMWxifSc97ylrdMwFabpcERAIfAXNpA+uB0AB4g1oimI70QSydptPb+++/n7vcginB+QEmCuMOIBUEP6/RbfRfAqJ330amYIioBA6vZBtw0atHKGAGhwvsAyYZ3G8QHhhkMOURDQ5xxPUSsYHzBUEUpAhQiGraAsHba1gpOusZ3s3tHTS/+Hm4wbjHfgDO6SMMAhnEQ4qSkodk5YWTg7+EGRxwwhKcfcwHRQJQNwGHVCVsr8xX3CYMTxj7mI/BBGQQikyAyYdpxq/MV5AFjDVEQYIbzI/KH+lksJzaXonOtjMVWZdN0qd6hDJhLc7YVjOLzYTKCjmWs0BQNDjc4HUG8QRqw1Fi47NpUcxXXimeETIU9HEf4eydsICennnoqIeUf9gnWisezYD6htn4quYf7hy7G8dja0RFxfRFi0UyedgJW04073CPwm2yLjxHs14qeaHdutnrO2cIUUW4Q6Mk2ZEBia1XOYV/YMJD1mMewAyH/DzvsMNaX2ocC+yFrC45L2JSwFVHSiLket7FnCxu77qZBwAj6psHRzmIIGAKGgCFgCBgChoAhYAgYAoaAIWAIzAgBI+gzgs8ONgQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQ2DQJG0DcNjpv8LEiPQa01apaRhmubIWAIGAKGgCFgCBgChoAhYAgYAobAlo2AEfQOfb+oz9ZO0XOlMVCHQmm3ZQgYAoaAIWAIGAKGgCFgCBgChsCcQMAIege/JnTSRLM1LGVmmyFgCBgChoAhYAgYAoaAIWAIGAKGwJaNgBH0Dn6/6Bx9xhln8NJSe+21Vwffqd2aIWAIGAKGgCFgCBgChoAhYAgYAobATBEwgj5TBDfj8QsXLqSxsTFepgFL+GAt7XDDciq2GQKGgCFgCBgChoAhYAgYAoaAIWAIbBkIGEHv4Pd4/vnnT3l3WI+y3Q217Y899hivjTrVGp7tnneu7Y8mfMPDw7zOJNaRbGUz7IiS4AZsDbtk2BluMjOTjDvDzrBrRa5PtY+Nu+QIGnbJsEuCm+lYk3XJRlt0VNJxN9Pr2vGTI2AE/Uk2Oh555BHafvvtn2RPPfnjolP+dttt1xIehl0EUzu44SjDLhl2hlvj1Gxn3Bl2hl1Lgr2FnWzctQDSJLsYdsmwawc307Em65KNsolHtTvuNtV17TwTETCC3uGj4t5776Vzzz2X8Pm1r32Nl1371a9+RTvssAOhiVy72+DgIC1YsIC+/5KXUE8uh9DUtKcII+3wsm2OLeVOitPjClWq82eGUpSmFFEquK7feeKd1FIpqhHhCHdInR+xXKvRSLnK3/dk0lSsVuiEq66kjRs30vz581t6JMXuey85iHqzXcExKT4vb3yfuGaV6vUa1flu5Duq679rDvYQy+jfiOcLDvIdnxIQpPQq/mqx+w6ASRHh6nLdutxfHefCHdU5oi1/ctd1n3KpFKXSxLhn0ml3FvnLeLVOx/3uprZww6UVu/c9a1/KpLPyjvkZU3qX7mnlvvG/Gu61jt/1WfityrvlJ3f4EFEa+Ljx4l4EH1euy/nlzQND91f3qY/Pv6ZSlNGx4+4K++OKOD9OAUzwHhhZfS9ElE0R5VNEuVSK+tJpyqRSlOWDovc4XqnSqX/4Y1vYKW5nfp6oVCH65ynkWScbApOMiHD/zTODm0+h+LXC2/ZDz7/PYAq5t4v7zmaICkWiU06jRNi9ea99KcvjOLqb8No6jngI8PvVf8m7C+di9JdoHGE84JB0ADIPl2leUlyMYbzLfcmdBlKlYez6PZysxO8sMyIpJGdwj1uqVunCW69LhN3CZbtQuZqiKs9FGfcqS/ReM6l0NB79nMBeDnF3KzqXolmMc9X5WMwrjOt8WuS95DQ5PNxzuNfCf8noHEilqe7mPr7PpdM87yo852WOQs7kUmnKu3mZScvc7EpnKJ9JU08uy3Ium3FXdYMDI6ZcrdClN/8pEXZveumO1JOHRJk4ZyPJJeNI5rSOvWhC6HgU7JweiMsyGR1+Aka6KBrZIq/kqvE5qb83zoloR503PA+czoBc9jJQ54j/TuR2uVyjiy57IBF2XzuFqKtLrtGiqPNq1qng5gLJfesxcLi2Kk9DkDHu+Dwy1NveVI9p/p6eZrxA9N7PtifrcHHVFaedTpTJE78DjH39VJ3L764mepXHA2Qs5mAK9pb88HgLdGT4Dvz3bjCpzaa6utJoUsi5cF62KeRHn70BAzVXvP2jdk9Mb/sBK7aRXh9fA7vT2sROcXvBDjtJNiXbHHIRyPTw2WFHQk7r5sS1t2HiwwH7FysVed5UiuVTfz7rZTvmVgWmmGIWjCLM13BYye+Q8gJoaIuHQ5Dv2d032yqsVyBnRa7i+jv09/O9dEGQuuFbrFbpWzfc2NZ8bXvQ2wFtIWAEvS24ntidr776ajr00EPpwAMPpN///vd0++2304oVK+j000+nf/zjH3TxxRe3fUNDQ0NMSC98xSuoFwS9w7ZqTYRJyRFJGFEgPSyqAkkZN15DgalCjYUWK6k6Fas1Wlso8W79uQyVqhV69+W/ZaU2b968llBQ7H70ikOozxN0p3080xNSruRchaNaDyDIE7S5ey4vKtVId+fUx06nxNgTQzxu3gePoE4CoeJMyllxqBJwSqZeExLsrC5RBmpgq5B32jiVZlORClWit1x+XVu44bSK3ckHPJcymRxfBoocl6/wuw4MBkfaK/UaG9lK1PH0qjDxKQpKlBb+z8qfFZNYF/hrzZ0LnwKCwymwMtwZRInxLnUxIjwhjwxoMS6i0SfHyv5ZIupOp2k+DP5UivJu3OL5cOJCtUofuuJ3bWGnuL3nfUSPDxJhCIOwekdD+EyxR+M/BeRFn90blVON+pat4thJ4oZGQCLDMahDTYwht+kUDww13D8cH5Uy0f9cKEZou/P1mGfsR5l0pmHaRTaeIy3uC08inWNGxp6OMwHbzz5v3LvvdK60JE0CAu8MMXUERPIrfAluDisp0HetZK3JNXU+Q9ade+PfEmG3ZKvd2cnFctldC749fm9OruTSGR7v7Fhz3+NvoaMjGovqzBBSDkKuxqvs496HGyBCXpSwRyQ1ByKPuZ52zgF3LzgfnDHlep3lCz7Ltbo4zChNIOcwTEHM+7JZyuEzl5O5noFMwXPJA+LaIOi/uOmPibA76lUrqDsPFzMR7H4h2PK8/h17Q1rGg8odscHFaaFzWA1znfKMb7N5GiMVIcH28879I+A6E0mCn7vqpBJS4FWGOim9EytyLGOfUrlK5/zfvYmw++5pRF15J7JVxToy2fAMofxzcmMygt4Alc53d3ycoE8nI/lSSmBbnO/hbl4NOcIaPsZogegdn2pP1uF41RVf/jpROoeyMgQmhKAXaxFZZ10bOM0whkDQlZxDjylBj0ugME7ixXaMJGsYQge5DlMQ9JCYe6LOejaIY+jYDEDC/cjcjM4R1yc4+ViB6ORPtIed4vaCHXemfAbzVexNtR2F3EZvL06aGUtnw+heKtPK1RqNg6C7MQy5M5DPeTsF1ylXGx2yas+oPI3Uo+ynwYjwPhgy907l76GNEjm6IAf7c1narr+XnZS4H9wr5DUI+n9ef2Nb8zXB0LdD2kDACHobYD3Ru+6///502GGH0Yc+9CGuGb/xxhuZoP/tb3+j173udZw23O7W6QS9wuQRCqXGxLo3k2GDSsK/0dNORdBDTCA4YagVqjVaNV5ksTXgCPp7rkhK0F9IfVk4NwL2o6Q6JVQTRp4IeYmW+zix/7fTQrGom4S15W8qqMWwgxKLfM9qHPCzxiW1J+jIQ2gk6CyM2QkinmD8GzfIZDcgU2L8iRHMn5uIoJ964P6UzWT5HeO9AC0m6O7fYmMJWjCuMQZUWUJ1QsEIcXYYqZfZkWsl6BLlBmlwyIfGlFNgIXQcvWQAhIZIFMEZyEGESyMA3tB2jiQcg7wAOJRA0LPwlm9Cgn7UcUQPrycadwRd2yY0GJ3qgwh9EUGEyA8TGDvTEPB2I0r+3MFYbIgKhAZxQOLVqAjHMaaQs7X5XWCmVUtEV/0imeEFgp7lrI3w5kRKxIm6GjZKLsXwiqLo3vAJxpAnUDFiNJ1s9hH2GEGPS7mAsnoZKK8v/K86bXSgR08Lgn7ODckJeqWeYllRdxF0Tr5xRinuDUYt5qU6GCo1zFvMPJmrKqu988PNJ8yRrgZHYF2i3ow3zuwIa+AUUaxBrHFNjngx4xWUhKCnhJhz5lSdSjWQbWTHICNICHpXJsOGKs7Tm8tKtgM7P+pUrYk7TyPov7nlz20Zrapjj1aCDnnlHBH81lSg+387x2Lo8HH/hrGtmRm+U4qf56H3LRg1k8ztaK65ERXOQzcZQsIe6oOJxFzGl5/jwRgWslBngn72/yYk6J8n6nZJag0R25iDKpgGkdAInNGKiowmt6lwiftr4yRsChnpTzGNHG2QAbpvIAudmm3YDSTzHZ9sT9bhBDruzgwJelWIOQg661un4z2Jdg4gT9DrovuY5KlTKcRNzZYQSh1HDmTvcA0ELOvUZgRdx3mwr8YwvI5yrJ4Jei10WAVBB4ctIuhJCfpBO+zMcoHHm8NJI+aSDDfR6gydkqEZJu81ReVqlZA5hw3ngLyZl4dDUGQbngkyiom3OkADR2c4MFSiq/0zgaC784UyNz7+ITP7clnatq/HOSoFOMxXBBC+fcNNbcm66XSc/X1mCBhBnxl+m/Xo/v5+uvnmm2nnnXduIOgPPPAA7bHHHlQoFNq+fpygsyHUJPKjKTHxC0z2fVNF2MLdqZBR3VWoVJmwDZXKTNIX5fPUm82wgeMyht1ZJwrLBkPWKWmIxkKtTmOVGj0yVmIRuDifoVKtQh+68vK2hJFi98NDD6G+nHPv+2fUhHJJzueYcEAedTena/xRaqupQNeocBTODkOQMQYWKrUGrKNUekpFBN37OJxVBUMUkTGku9eq6lDQ/DMh5zCA8ZlhT2uKxip1eusMIuifP3B/SqUzbIjjvbCTQBJdJYoOZ0qtxiUJmqYKpYI0Wnj25d/yqZFuUejiwFElp2Pap/26Me7T/hvfgLpDvAskTDtkhegNZi25EKqG88GJwIY0yGQqRT3AjJ0JchHJ/kixJ/2Tv7s60Zh70zFED20Qgp7LNkbGPZkO0mD5wmFarL8X+V6Nn4ArNM7WdgzPRivCPXNk5ERfuO9iJB7v3G+ASi08lGlUiSpjQtBv/n17RqsnSs/Yj3JpjJ5oiz+eOoHUilciLkPLkZGA4DeSTn0fclYlVCqPlMR4GRAHPYhUagbLBEgbGEY8EaQxqq94q0EHgv696xMS9KW7eyeX3BPS3TWaJY4LEF6WEfxJVKrK/J3w3M5oZfLLmRFp6nVEG/IFsgBzH/IfxqJEsWVGg8yLkS9yqScrTgGOoLsoZDolkXKcuwhi7qLnMIAVEyk9iQg60tq7MxnJ6GHHKiJaUhAD0lKtVuiau/+RaM4e+5oV1OUj6E2yLIJIl/gZnEMjcKpp6n+D3lBWp4NY1ULjDI5JOO/3DUZxFBkXORWU7nhHic7jYN/AOaPvWLMpPNSIyJWr9J1LkhH0s08n6lGCPtnEjTEU/jUg3/E55Plxs31iAiF0UGr0Ng5v6OQMD3f8TqeLy5xwejUg505E+8lccycZGyc67mPtyTqcS+XdF84iQoIfO7+rqlOJU6mVpKsO0CyMjBJzfDqTkAl6zAekc63hlUynKxzemg2iWSJapuKj8tgvcM7q69TP0FbSmI1m8+grHUcp1Mfbw05x23+7HdjZyNdz70kc9+5Vhs8ZZpe4v4sp3QgGAk6FCiwZ+Ruch8jciXaD8zNCU52cofPLqwsfIXelMF6saQReRpTXZbHIjU9xB0HvdwQ9CMcjgv6dG29uS9ZNInLs602EgBH0TQTk5jgNmpf95Cc/oQMOOKCBoF9yySV04okncl16u1tI0OM16CpctLalWQ1lKwTdK54Wbi5+rbFKlY27DcUyp9ws6e6i/myWBZtGDcVWTfnabL1MVJOje0h0drxGNFqp0UNjRTb3lnRJivtHrtoUBF3N8EaCzlVM3mKIQnViaEVC3CtAH2nS6JFqCWcNi4iPcjwD7dUQGRT14swtuJsbC5y8TYfodA31TzUm6NUqauYDVcgedNRzIoU0JfWZqRSNlWv01suvb1uI67gDQScQdDbGETHT2lapc4VZPlatctQLxgR+l7rSNBvMqN6CkQ0irDVzGvluQDZkraIdGROtLdMx440290WUwh5V9CLtVqPpUh8rYQd8x/V9LvmXHQeE2laXxqsRGqdhx6sV+nRCgv6Go4keDAh6swi6GlsThoqLkvh54gh6yBMbzIrpDK4p5nWTId/ga1KbIYq8iQHp34cz0vQZahWi4jBRpUh011+SGV5H77Mf5TJNCLo3Pl2EOEbA1TAPUw6jcSMgxaPnYfRCHyvetyOMoEayayph2TjD/Rx2L1D5Wih3Q+fATAj6oiW7cSgtkqhRFE7S3onnphJffKKcSCNPIUkPI+iYK6gL78+C1Kc5soT5j7mP0hboAZxbEk7F2QViDj2Aa/RkUTfuai25d4Q4ybR+NiTokCUS8RfHHtefZzLUl0emS5q6shl2wpRRUlOvNRB0OC6vu+/atuSdyrrj/lUJuqaqRzXmqgdUTGmkXH9XrHw0MyZLGnKF3dCJcYOJAyryU4iW0N+VjMSSsXzU3KfPRllWvp49qDvnCKc/pxD0bycl6F+KCHrEkALHVHD/fg5Fqm/Cszdw+VClxhVBDMuG6H2gc1U+xQ+PcbimTlJ9Hq97nDdY/ZLjMyTon/sKUa7blY8FKe5c8uF+bxg/mDcoEQt+2CntEvY4MBKklsedRY2BkygtwT8fzJDI7+/7R3BNOv7gSLm26wnJqX+l6jRwJ/XvJUboEUFPStAP2H4HQrlOODdgP8ZT3MPBNdH52qg8keKOfkcim0X2wLkYEnRkJslzyihVZ5d3DETa0emcxuCUOhHU8SFZEk4DiOD2+EOGosRz275eQplQPhMlwxtBn0oHz87fjKDPDu4tXRUk/K9//StddNFFtPvuu9N1111Hq1atoiOOOIJ/TjnllJbOE+7UySnukCWDpQpHztcVyhxFWd7TRfNyGerKSPMfEWRBxKipKo5EGxTSULVOI5Ua3T9aYoWwVVeGKlVEM5MR9B9wBF3r951QdcmZmtquybPeEAuIuYpXCNGQJGlelcbronovtSjkWmEKlpO/MRSEoEdE3Yl/p/nU8SIRdImeV6qS1inGvqSCgqAjKibNiwT70XKV3jIDgv6ZA55LqUxWomRMwus0WgEhF6Mcn+McQUcEDFEtIei4D9R3d+Ezk+HSBzR9wndy35G1KU8uv0fpocrGHIYOMcVf3wkidXyM07yRAymqSYeRL+/BXbkhuu5SBF20nw1ttz8i6B+7KlkN+htjEfTQKAqNemcJNBqGYh3I5lJEtbHWhGN1JCUk6UrQvUEcVWzI3PWsNWju44Yqj2WNoDsnAhP0ISHodyQk6Eftsx/lmaBHsyV67wKKRsl15Gg0U1MdNdU9gqeRoEfjIWwWp03fJhfT7iyT7uCJdmyPiJRrZDbcIbou5gEI+vev/3siktk7fwVlMqjeFiLuGyQiOucIut6L1qJz9otmx7j3zQa/y4TheUl1jop3u4g45jcI+jgIek0i6ThUS1Y4cs73IAQdpBr/lvcEh500jsM58Vlkwo1IPGpAxciG4SqRftS+I7U9wwQ9n82wsxL6RvYTWQgDtlar0m0PtOeQVB37rtfuQt15cQxFteSa9h+9L1xLGjs1proLTtpbQ56Np4rOTSZ2zdPcw9EwgTTG09E9AQ9LPhrrzZk8epnoNIvDVNRKlPKu5Q8zIejfOl0w09InyY4KiN7kU6qlvzREbUP5qP8O5KUILjntRNI0+eVC3dzgIIgdopFzGZ+SLXTCDGrQTz2TKN8jF1HSX3LEvOxS3tU64GcCtu4Lrd8P5aN3Ijn8w1ILHrv+C7mm1ynuOdWu0GAteqiIQ0p+0PJB09pDnH1JhncCIaOFqFojKpUkZR6NBFnHSm83rkH/bMII+uv32IOyacmmQRYQbJN14+NUrFR95k6zvM1Gi8JV3Lg5AZmGc6legcyC/eIDM97qioh52IQu1CvaRHSCjg00G+PpUuRD0aD6DK8KNfA7zevzKe5qoxZrVfqP66wGvSUB8gTtZAT9CQI6yWVKpRKdcMIJdN5553GEM5vN8ueb3/xm/i7j0nHaOXdSgo5JPJWSaeceJtsXAnl9ucJGFRq6jVdqtG1vnmt6u7NpNphESbZG0LEXaq82Vuo0XKnRfaNlVgjLu9KcunjS1TMh6BqRiwi6GPGiEj1BdPcqCeKTaH3fvU2NHEk2jAyrkKBH32s0uHmyf6SCvX/WEXQV+p6gg6hXal5pIKKFiLkQdZfn5l7aSLlCb7n8hraMfRyq4w4EnRxBZyMa76dU4myJoTKyJkDQxbhWgi7RcyHmSB9H9Kw/C6eNEHUxYiOCLpFyUZQS7XbGnQPUOyKcMaYKCkoT18ExGpVXZcdDz0XItQ6ecRcGLtdSou4MLUnDl/pTbKOVCn30yuQE/eEwgh5a3WFiRZjWHjzflAQ9buMrQAkmdTwqx3MhRsrdBBGyEUSzOEVwEoJenjFBF8PLp6aH2DQQdJm74WxVAhJmX4Qp7u5UjitpKY42MYxkgXpJ4r6PqUh6+wRdrFm9VzE2K3ROQoKeH9iZsw9AIEFqmaC7B9DeEfo86khDVk5I0IXAi+PLO7dcCnkXR9Hwt4iga1NPfgvOOaip7ZxJlSKe+1LuIsQWGTXIsIGOwHec3o6IfEUcf0z0gnnJ3ds5TR4N4ySjBw40aclRk3IVEPR6je56MBlBP/51u/oUd589EESzwnHANeqOoHvnGze+lGZ1HPl3Vj1rGBjigZdOCVWzKavX8XNTFJRz4UbOHN/VOyBESrwlMh7sq7XMLmTekOLuGgXOhKB/8wuu+zhStJ2zjlOiA5Lu591UciqQ+X63QB7pdx6jUEXHJ2pA0uN/anYL3l6KkftwX9XS+smvtEL0gZPbyxYKdezJZxB1gaAHclwj5yDoSAsP1paJbsfdBAhzSLLDLA/RqdEmY9b9HtOv+n68XnTqMhcQdBzSjKDjWL7HoMIPmeIg5uUKUWGcCG2ABvrFQaA9QFEe8LmETeKOevrelE1lWHaibhyOxkeHR2msUvZlPL4JrSoxbxc3jgjVGZAriKKrPSjORVeW48Pogqg2I9W55rWVG/fg+ThvtNpH46hT0i8lllGwRd+D2iiogd95nnRxRwRdgxiwwb5pBD2B1bP5DjGCvvmw3WRnfuihh+iWW26hkZEReuYzn0m77bZb4nMnJeiJL9jGgRBmKwtlGqlUaU1BiPpOfXlanM9SbxYEzS35VZ8YhZDLhJpXBCbS29eUazRYrtEdI2VWLtt0pahWrdAZf7qyLaIZ1aAf7DvgR9WpSGkX0cgGvldWmu4cmvyRgaSLinkzXhu2haTT921vVKtxT2sUJg18uqwBHTouyhEZbBJZ4p+aEH9ZRgxKz/VPdg+iaVQjlTIdcUX7dUq+Pu55B1DNpbgXXQf3tYUCjVUqtHJsnEbKZcKiJIhhVZkMOFeFM1BhUCOFHM3YkFXRm8myIQ3yzk2nnAHPtajOsGUC75vRCPnSWtX4Mio+uyFkji5KwJ3ZEX1LIaVXIuW81E6UrybX1HQ2YSaMJ04HB8T7r0xWg44I+qYi6DxTdHw6Iy54BBks4d9bmcNhqwRv/AfjXI2/GCEPjVhP0HW8IoIGY2yGEXROcXeRkXBe6mMFU0SmCt+IgBCmCsZLSfyMDud64Ozw0qghDzJYSsvBrPvJq2i0dv2hzonnM0XUJnb3GZmHzmEYXJMj6Dcki6Bn+1a4yLmQbFxHpRBWdsAmc8t1VUcE0BE0vQUfvQkIOjCWNHeV3FJmoA2TZAlGKbPBReGkEyKvkTe5HuY9N33j5m/iROBl1tAclIiX1oRTEVEs/OBvOZd9A0efwo1mmXAOQhYizR0bR6xrVXrw0faiSirrTvi3XbmLu4wkeVA1ojVi7kTExPpzHZzIUqjW2ZDuycqScixvMDeCcefnazB3VSWGWjEiPElx0xQAACAASURBVEGWhdvBp8q6m2KHM0c3JfvAk0lt6MlYyXxxvUaFPLgpVChX6Ds/TVaDfvqniNZulJ4b1bBnRtAkzvPP6ZMIInmmYDR0Motrzghbn63gdagTj03kYzzdWXHwqsQLu0ighp3gQcAwXAbyRGd9MzlBPwnlAb0uucKBpI0dtSa9oVmcNo/z79Vli0X+56iUJ0bQw+HGxDKQ79FYl/NpzXku3dgwTo/x79OZLFoyofitXkt0z/1ExSLR6DjRwgVEe+5BhGRGbQmEFPcvtpl9oPP1E/vvR13ZHGfTPDYySsOlMt2+fgNtLBY5YzKfzlA3Z+6kfTlgYGkFajPSKEzQuR9H1KsGcoqDH962aszganDkMm7S20Md/tK3ByU9De5mt9qNXIeD9p6oywTBXWkX9+1Rg879NyLnAJbjPPumW9uyiVsxDWyf5AgYQU+O3Zw8stMJ+oNjJRpSgl6p0a4DXbS0K8MR0yQEfaxG9HipRhvKNbptGAS9Tts5gv7vf7mqLWHUSNAlgu7rhnxTOBcGDDoYc1SwMXYXRCPcsmyOGEjENlruK1KAiKSIaYxl07wnNUzbbXTiTug5GqXx6tANQyVBdCJYR1NuK+qmDmP3yKtuaQs3nEOxO/2gA6mezrABjSgXIm1rHEF/aGSEBktlIeYgRyBVvHxTsLY9Sf05N3py0XR8ovEKp62i6RQb6kLQsS8bB44Mca2qqwWTdd71zSixcW/VLSckkNaFIKB+jJ0DaFhX53NyJEtTT53xosvBqQJmY7qO8o0SvefK37eFneL2xqMdQS9Lk7iGur82I+j8RIGB6ZIAGuVZaC21KukCkh5PT1TrXo2tMDLiiYRG0J9ggt7weEFToJDY6Dzw+wb162r8+LR4t5OQ/TCC7ox7d0A4XaOofeDIC50ePNa0jZc7sc7whu7xm5agZ3pXSJlLKOtSIn+0/EWj2JFUaWx8xI4/JddBXpCkuWtUSD61rt0vJemMWU5n11UV2LCVvhDoTwJDE5k1StDxid4V+BkuV5iko/koomLSIA417xnOxMGTgJxLpEuaViIDQIxZRBur9NjK9job+6URX78rdSNcyFuUycOZOGHWTbDUmmIo069ONdxTpc4ZZL1gNgKmNNR0Ke6RSycyxMMxHa1CIN82RtKD1HTnyVXngUbqgE+c0KkDhVcE0ZVBwk90DkeTuJ/ek0jefeajRI+sIsKSYxVH0HlkJyHowUTzS7DFyHJEqdx4jF0rnhkUyszwWD8HVLVqzXezund3DX4m9DKpEXVliRb3EX3/nJkTdE391nfO13ARdH5nutxasF66foeUcXaGBY3iPOGOJrofZg1k2jk/JiPocELwuXVmuHehKsf7UNRxAAxrRA89SnTtjUSFItHYGNFWy4ie+2yi7m4h6LgeCPqXPt0edjpfT3vBgdSdzVGlWqd7BwdpY6FEN65ZQ+sKRZqfz1NXNsur9yD7Jqzzjtw7E6PWStAxl3h1CJ37LlsPEEjTTY17R45QGR5i6wAvXbkCDkTsLzLVOf5cyRE7Jrm3UJiN6QIFjqD35TK0TV8P20hwOGDEc9ZSpUrn3XJ7W/O1VdPA9kuGgBH0ZLhttqOwpFqr21lnndXqrn6/TiXoEMpo5nPz4CitLVXo8TFJcd9nYS9t15ujRfkck3RRNs0NEQ0VR7o3RSPVOj1crNJ6EPSRKjcT2rGLqF6t0DevSUrQX0DcYE/uxv03qjPyCUa4Td9cJYqNibIXY1v2ldpH30wqyAlWgsjKi2sjRdNHBF2VAgS772HeUI6gkY/4YIkMu8Bq85lxkeCXCIrcH7zKR17dvpdVx93XD34er0nNCss1gUPdKbzM6wolGqtWaEOpzGmpo9Uqd2P2BZwu8Z8jdPwwLr3cKSpE0nqZqKeojyNrKeqHMY6lRbIg1ah5FWUnXaHjplXEiPRdaOMVblSHCH3GncMRdPf2NODK5J895L7+XYxp3PN6lKxckSyCji7uHEEHQcc66JPU/TlIogyO8CU3vPAgwhKvGwyZY8zpM6XAiUXRJdXaNRVDJK4W1R0qT4NRiu7COk98mM69Xo6gDxJVSkS3/zmZ4XX009EkDjNfi2PkKZo9WkjKeRo6Mqopwf64kKD7dEJnUrkT6zSeWFMYRFQ1WuTvB2NH4zJyEVm2DLWMVSpVqkwwe3KZWMq4Po3m9ERvSiLoybq4I4KO9HOtzQ5lCWrTQZK7M4hwR3KoVEOKeQQCyy6fASDeIZxHMlz0Pp0k9MJbmAGi9L4nhq7SoM4lIolocYRJUsF7EB3nfiXIcklLJN31uUB2jqS6w7gVks7RKDWQ/RruujSmEPQNa9pzSEYEfTdP0HU1OHE0BM7QgLg3ZLE4HYHoOUfQubFdmjKZFGVz4qqV1lOR8e3feGxg++h3kNYuejRSYUBfSVMW10inqFQVp4VE5JzOgbOUCZ1bJzpG0KXcVkqMiqUqffeSZAT9lI8SPfi4REo5gh7mx7nn0LIjnZMTnH5NhBWGYSw5SiH0kX98EU8Bj2aXh9zLfNYiIeF3aoXvGdfTRmYxjx+f07FS7Ac52JUjWjZAdP557ck6nErH3ckugt7geFWnZ9BUTWwI5xhzupgdW64hm+IbVweqekJnK48JWU3MY6FZa3q8NoXjCLpG1IOsEk/Q9R25dz40TLRuPdGjK4luvk0wg/m1fCsh6HmQc9egoFwm+lLCCPqJz9mXujI5tkXu2TDIkfN7Nw7yikLzQNAzGf7szqKHjqz4ENcLXI7ns300K0hKfpBCLjooanjJbjjVm24QiRyQOYRP7e3B858bdspfIPu07Ai/I/sI6fTcvwc19G41HDmLKzPiHj5pWtSVYzsFAQ1sHDCpVuknd9xtBL2J3Jitr4ygzxbyk1z3kEMOafgLGsNVKhV6ylOewt/fddddXHu+77770pVXXtn23XcqQYeXH2Ts7+tH6PFCiR4bLTFJe87iftqpH1H0HNeii9JrjaBDFA5Xa/TAOAh6nW4brbJiWNEFo6dC/5mYoD/fE/RIcWsX98ZEWKWBoXdYI+Qi3XmhLhdRD93skTb3JJ9TL5Wgi2EpRF/rF6VGVAWyGjVutwmMRAzFGJvwy827Wnc2tCUFHgoJyuqo3ycn6N855KDIMHY3qtEujnTVarRybIyvs7Fc5rptpNvD+aCRdNSmY6zAQMVaxzAotJkcCDoi3PNy+EzRAtSq4xNrHoO4p4XYIHKHJ1MlG08d1npLXdINxwpBl8g91CTcRbLMWrTmM3eY1+7SDlss2sQNZ4plOv6KZDXoTNBdyqdG0L1N2Kzu3KUnivUZsFElN+67kBQ0EIQWibk/xhsZzhhwN6e1v5xiWSVC2wy2Cdw9wygtVVxg0A3YKK1bUtzRJA416DMj6JqWqAb25A/oG1456Rra3ipw46nyamzHa9exv6T9RqQ73FcNWjGg5GW5VmB+LsPogvNyvFyh0VKZ17Fd0JXj7A8Yg3qUTCd1GEaqYSYEPde3wlFpRGV0pQc5dyaT4xT0vixRV7rORjKM8rFqmgpcOKyuSnEsqhxSGc5N3/zYVKeje5oUvFBu/XAY874hnPxdDd1oDXbBGEtyYo4i9V3WNObke0JpznCpwk0oZQk3WUKSo4oqR92LRqRL7hFyr0oj625ry2hVHfve1+9GXexNk+fUZZbCGnNP1mNp2t5ZK/UCLHvgaMxlU9TdJctUQvap4S2DYOKYFhImD+ayXr1buIFU1uFkcWUHcHBkUlSsSNo/r/aBSH6wFKaWMWj0j+c3ryHvCLqLoJ+dkKCf/FGi+1c6gh6S84AAxh83nmI+wThSkurW0/Z/d7LSE2pHXFWPqgj1sjSEWt9bjKCHEfcJBN1dWN8WnoMdLo6gL59HdMH5yQn6KUGKu461oM2N2Afu4krQoT/5HhxRbzKUeLAJuZQHkHcuS2EqQdfn5v08k5cXCNnPkWAQdG74GDVA1Hmg8tCvBkBEa9YSPfwo0cpVRLfdSZTPEc2bFxF01KKr/ESdelKC/u5996HuTJYdU3dvQGp7iR4dGaHRcoUGckLQ53flOfMG5JedVK4vA4sx1v2yhKOsOJHmucMr0nATXKAsji08L/Z12qhB06gtoqU2TNDdyhPSKFkGWz/3ZpIl2yDleHlLpyswb0G4kTmk45hlIdedwz6SksC+BoJeof+9+762ZN2EOWZfbFIEjKBvUjg37ckQIf/d735H559/Pi1cuJBPvmHDBjr66KPpoIMOog9/+MNtX3AmBD3uJNaLxxzDoR5rGqVqdtNId0bN+R/WDtFj4yUXQa/SgUsHaJf+LlrenacF3BEXHkdlGXrl0DCJDFQonI3lOt09VqENlRrdNVrlmsddetJE1Qp965qkNejNCLqwC1/apla4Gqms3CQtUyk0mhExQa9XnKD3cWG/jyjFMBXdCXhRSQ1Q6rWxpjAUhNICNTaVvDcsnxd5GKKzede4uwL7ESSqBOJ85B+SE/SzX3iQW3c4Sq3kKCul2PMLpQdSDmWDTzWm8crR6AaNn9CjAGUQo9Uaf4Ksj3L6WIpSrhYdkTQYtPOyacrjMycp8PNc92dE0qXxkhgNaDbHHaLZnNcOxjU2JPB3nAMKDftw9N07QiSzQOvcVTGHc0PWnk3RYLlC77g8WdbGm97evAY9InbOPte6QbUqHQGK7+cJZoy8+xEVjIumQkanYGhaqAHsLAKMF14ei4j68kS9SDpxaexIU0T6KkhGAQNAU1fZ4nMmiKtBnylBP+rpz/Y16OGzTEbRnZ9AIkEugh4a/1ORc4VdjSI14HS2qiHXuF+YDiFd8piAOlmxYbxMGwolSdMuV2lJTxdtN9ArTdJc5Fo9Hmq+eYKnTeJuTFiD3r9CnHicghs1O8L9p9NY6ixNO/bnaHF3hvpzaLyWoodGyrRyrMLd0wtwviC67R5RsBUZrhF0EZUiX7SGWbKBUpTmZpWOqDsjOJpbWoep9L8u5Seaxu5KYBBpwsogMFrh9IUME+KqrhDN4BD0JKLlsoZqVRpNSNDff1gUQZd7lpQSXEWb3fGz1+GkkhU1pJGec7TU61x3DlzZBMea8+jFkk9TLSVp/Jg6ZfccWpOubUT9+Nbncaa9EAt3R27da1whn4IjAI4WCXEW8P4QQWe5HDXs0s7qnH3giLnWoHOk3UXYS+UqfS8hQUcE/f7HiUbGo2i23rQ6GkIS6Z0/oZHSTHC55+D1s4NJiHMpQcfXGkHXU/hrNXGG6qttsIdCh+Uk6e2hrAV+0BPdOaKt5hH91wwj6L29QR25WgrR4iRRhoDLctKxBCcBZLKLAzQ+WrjUWTgeXBd216y8wSxR9YJPt1Ir5V0EHbYYR9kbMkpcVN9F5MEvH3yY6ObbiQaHiFatJurrE3K+bCnRvzyTCK0k9LUXS0RnfLI954baxEfuvaeQ6nqdVo2OMaEeLJaY6EodeEoy9LgXknrH9HHlbbKDHjJLXIAcNcfKNyDokEFeLeuKDS64wt974q7LMsqI4lT2oBwPzSzhLIWzoDeX8ytb4FrFiixxCScD9sG9i27AT5rqWWmqm0/XOaOw36VJILaP46544CEj6M3kxix9ZwR9loBv5bLbbrstXXbZZbTnnns27I6GcS996Uvpsccea+U0DfvMlKBr5KKREkeGjldoGrjzLtWYxe9JthyByOl4tUZXrh6kh8dLtHqsxIT9+Uv7adeBbtqmJ89p7kLOIy9i9HDqdxQ3LwQ2jAoQ8ztGKrSxUqN7QNDTRLv1Zjg09+2/JCXoB7kmcRMdA96E1YZKLhVQzFJRSBDiYkDXOEJTr5ddCmm0j7cdeD+XSuWMH1hAci7BQiN+UALYO481gp0SiQiCM/jVQRAQzPCdiZUiBBW17lKzJ9oY9YZDpQod+cfkBP2cFz2PvdTY1EwUL36U2KsOBbx/XU8Z+yD6BSW3oVzhaPRgpUJrihUaq9VosFLl2vUKR7elBl09xCFBh5NHUrvEg9yNdM5Uihbkc2zcY1+8o7prGCXrK9cleuW7LLue8by2i1IOJaNS1y7PJ8YGDC/8e7hco2MvTzbmQNAfCbq4x1PcNSIdT3H3KZRBhM7p6sgh46aU+pQCu9UbPnFBEye3cbtYO/PCGMPPol6iRX0SEcfP8BjR+kGiEho5uoiOy5CU4mF3E9okrowU9z8lM7xA0HX5nCgXtbFZW/h8Ov70HSphd3Eaj9sEoh4DRYleKALjdeoirDSlwLnUUlUmrCkn61aOFOjxkQLXCBYqNdqmv4d2WzCPSW9E0BUyuYmw4zwi6OcmJug7q8uK578Sfx4yHNlJ09MX9dA2fTla0p2hgVyablk/TvcMFmioBIeekHPMCe/4cKUqjSnuQolgHMsmRm4GHY/dnNTVEkL5HxUWyVG8zBrGHFLY0ykayOf5BwYo9MHwPxs1bigUJevGl2S4khTfEUz1B5agrNL4+mQR9A+CoOejOnfOQkLKel1qyrkrOw+yOo0Vq1yzrenl4rit04KeLC3uzUlqfhUEPSWN57hJnJD08Zoro9HTuYncuJSY5mlxQZVfrxwvJQuSjpTZVI1yqTrV0FE/nabxKpa9k31BvOVWxQLQGlwfRQ1T3h1Bx/N8PylB/5gQdE5xD6Lm3lcdc083EPRpSDo7grROOlB+/hyYP4qliiJ1hMXkKIupgPiGkXP3alV9q/jwoiYUFxq9Roo7Iujnn9uerMNJwxT3voCg82xyMl7vz/cU0BR3F8VHl3fu+B7Uzk+Q/dGw9U4NxbQZ9FomxinusE+QReUaRGpZRYPodPeEdPVimeiue4j+eh1RoUA0Oko0fz7RjtsTLV1KtPde0s1d08RRg35mQoL+pqftwXIKYx1yAtFoOEVltRuxV7h8xvW1URtMgiGiLaKVKiR1HX0vBotlzqCDLSPvQstcgk+Xp4SxBNkQOl/jjUPRy6ZYqdDC7i7qz+XYIYBofAEBjUqV0+9B0BGBr4CguyyiGpwPKBNFQKFeI1hhIOh4DvR5gP3454cfM4IeH/Cz+LsR9FkEf7pLDwwM0KWXXkoHH3xww65XXXUVveY1r6Hh4eHpTjHh7yrE//sVrwjqqFs7jRpDSpHVetcUZTXA2LByCtSZnhMu4IWOixZgGTRERC8HQR8r0oZihSOq+y/ppRV9edq6O0+LclleBgM1h8Ic1XMaESL9FwwweIJRe37LSJnJ0aPjZY6YbteboXqtQuf/LVnDrh8cqgQ9eixfNxQ5VvmP6wsFrtseKpVotFymBfkumtfVRWjUMZDLUZoqlCYh6IioiwEfdeeUNEupdyqUJZoBz66u2wvBP4bO55yCLs2Nturvo37U7OezvF5wiFJIwtTgCoD0DehgTHqt57q84ytEn475c3sGa2g8XPDigzgtS+k5lBgv1+QjV66TvCb6Ojwx9sYQAavWaF2pRKsKJdqAXgXFEq+nPoIoD6JJLrot6yGnqEs/eS31FPVwHZc0exMCLynpIOiIqoO465JNElWS9FSQc+447YxqRPjgkfbLIDlywApYybkzhLXyDJ70Yy9PmOKuBD1oEueNIWfdhOmyPq1dLZ/gkyOSwTH8/kOSHsuWnS4oFY4f3VdrDBfkpSsxfGK9aSJk+cHw4khNFUvPEa3F0jk1ovGqa6DuIupMQlwN+kwI+pF7P5uyyK90tbFh8omXZYGEiohk5ERS4z20ruVYAVLTDCOZpJHYUEpFBlkoEOUcIFkuJZxDKXUqV2Bg1emR4VF6ZGiMujKYz1naqrebdprfLzMkpVGZqOwndDDgOiDo5930j7YML9UTWGZNYt2aVRKNBjRNxJx67tJe2qk/R9v1ZWhRV5ru2FCg+4cLtHqsRqvHa9JEMZ2Wd8zRWGTLONyClH5200naQpTur83h3ICNZKNDMCRSLltD0mhl/sOARQdmSb9PscGMRpfQLXAAao8EjtYHBB1n50gzR9Dbc0j6FPfDdmPDuVqt09rBIhWLVRoZr1C1WqOlA3nq68rQkv4cf+L78ZKbABq+rNdpcX+Olg3kWd5jvuTRLM512UJ9PmReoZ5ih0NZm42pYg5bhHPNutb5u3HmXmUXZBc/sCwyDScnjHmssoFyIq3bF3In41oJnhJmHeXcUM6l1YOgn5OUoCOCjiZxiKDHymdCO0PGpYwFTfwKvwvnmf6bj/epbo17hHOfz+PG1wR56WRmA7GMyZAw8S0IuDYsYaayM07Qz5sBQUeKOwg6l4jE5LyrQGioPWcdrM5k1xNE1X8z/LyHMjARQk9uGHsREhtF8yUbTQg6Z69Fqse9xCgaDgfu2nVEDyCCfhsRounQHejevmInoiWLifbYI4qg492NjxN9OWEN+if2f7b0KsG4r1Z5/oNTY+6tG5eVZgqVMhNtLnnjxpSSsSM6QHvPiOxA/waQ6ZUj4/J7Q2AlQjZSzSgxgVMxy+fkZSBV7lGK5Ri+Xzkyyufty+U5G3FBd56wdBoi5rrEZdRs0znY6kRj9RqtrWKNHCLM+e50ipZBNiI9nmqcxfPr+x5oS080HR/25SZDwAj6JoNy05/oiCOOoD/84Q/0la98hfbbbz++wF//+lf6yEc+winuSH1vdwsJOtJjWtnUJON+477pmX6bYuHOabwuNYxrCzVaHBPAYn5Fhi0MUZxzY6VKw5UqXbZqiB4aL9JIWUjpsxd10069Odq6K0cLUXOTQaqydsbVs2l8Wp4GhiuMEayBvrZUo5uGSzRaqdG68SIrhqU9WarVKvTf1/6pLWGk2F1w6PP8MmteuLp/NDSEqtfpgeFhWj1WoJWjo7R2rEBb9/XT1v19tLS7m5b39lAmVaFcuuwi1C5N2zd7k+g4DDoQ8JESUpZqtL5Q5mgyaqSQ1gQngKQySar1bosW0OKebtqqr4cG8jm3DFIUB/SGStyi8YaO1JzLn8U60ugZIujHXpOcoP/oJSgP0GZ/QoDwLFGzJomCwZjmdZVd7TluDU6c4WqV1hRLtHK8SOtKFXqsUOT3PIY6r5REZLnW0zWBi7oli/LklGssl+YMBE6BT6d4bKF2dZ6rYQdZB1FHhB1Rdd3fG4dcz5VhRxT2k0/1hUh6rBBzrXElTq899vKETeJA0DcSFUrSxV0j6NEsnDzF3fMOTSXUjLeQvKshp4ZnrCY2LifClG82ZN1/FB80DgOPWN5LtLiHKFshymAd27I8A6Ie+W6iDUWiR0YQaSAaKrr5G61yhkoUbhI3Y4LOy6zJewm3CRFt9yCa4hp3ToS/q7NLWzM2RNIcoW24VtBtPao39/4zSTtFirHbb6xUpUK5Sg9uHKaHhkZoYXc3Le3r9Snu8jyIkKjci64W3icI+vkJCXrXvJ25oZpm1WikCtfMICU6naKDl/fSbvPytKI/Q1t1p+muwXF6cLhEj4xW6JHRKlWdG3KsWqchOGgQWWIuGq2f5dZtEMdu0IBP1/qWPhRxh4jM9SgVxA1C1wQSc783l2VDF1EmRL9QD4pUd/yAqEuGi/TxgLvNb5oWjhr0tckI+rtfvyunkZbKNbr/0VEaGivTuqESO152XNJDC/tytNtWPbS0P0eDY2WOolfgNECU3a0KsmxenrZZ0MXkvFSpC0HvyvCKEkj/V4KOsVN0DijODOAXFTmu8f6kzlg7souuwB5YMhtkqVSoUAXpuJk0k3SY8hWsouFKDxib2BJ6TlzwS8OY813dIdc3JUFX3eSmS8O8bKwAa2zY1sTACZLSYu87Iod8/sB7N2GYBcw8TtI1XR6naCC6YfM1l9LsMXU2FEfQ/xkhPncGXdxP/RKRpriHqfn8fjRi7sg1IsPS9E8yFbRppzphVMYxHKEzLHgB2Dd0dGqSozaJ0waJ+nuXI+jxIE6oQ3C9Rx8nuv8RosdWEt11n7x43MOihUS7rSBatIhot12RZRO9N6yD/pWEBP0/XnII5bNC0HWDVIAtdP/gEGferB0f46VguUEb+tHAHnUlhawHnDji6Hu1SmvHC3T/4DATdMxLxsm/lKgvh1xPek2AcCObD7ab1JyLPbSkp4eve+/GIT5vHkvMpjK0dX8PLent4gCPNvSFY0Ec8dL/BiWkG8plund8nG0gRM7RSHf77m6WA8hghQ150R3tNXVsMr3sq02IgBH0TQjmpj7V2NgYnXjiiXTOOedQGa5DKNJslt7+9rfTmWeeSX0oxmlzmylBZwPcSTCtTYNgZ6PApZZL6qLzJromZrwGJq8/LTfsI+u8BFWd1pQqTMB+u3qYHh4vU6kuDXx270vRsjzRklyK5mVSvNwFIs98drdGZAYNhVxMVu6vxt7K0Wqdm8PdOirp80OFEpP3nhxSlir025uTRZV+cOiBnqBPhN9RFKdtHhwaodXjBbpz/UbCv+H1xM+ynh7apr+X5uVStLhbYlTa7gmmkyx7gRTvKo0Uy0zMNxTKbFhuRLOjao2GK4gE1TiCjnQmoAB8l/f1cPR8x3n9tKinixZ04ZpwbLjaRy0SCEMhrku7GBRCZNgwc2mt+j2cBO/+e/tLcfjSipcc5JY30jeGsSP1p1o7CUIAJabpk6g5x/tbV65wejt+1hbLNAIsEGV0xikbIJ6gu9rVKCGZX5VE6CLvfTfXXSLCK02Y+lxnVO4G7SLuqF1ngu7SwVTxwXmga68jqwMeaa5B8+UDYjhoKj/WBT4mIUE/HDXojqAjAcH3l4ksCTGgwhrJ0GoKyDgbm8F+ofGlx3sbYhL5Eieq2tRHUxb70kiZJepLEfWkiLpA2NOyHE4X3Pcus3v1KNE964lrlccg4qJVCiWNt0I0vlG6uN+WMMX9yL2f5VPcOYU6qMGNR2RlyTBd0xuRXumGGyfqISyRky401cMIuhpmclSUsqi/y6cQ9GgdqeHxMRorIhI9TqtHC7Sot5+W9Q/QvK4cLYVBxtHMqJg2NKb1/iQaVKH/Sijr5i1cwW4/jrI5R5o6NVAuhJ/nL+umXQeytDNkdVeKDJZRawAAIABJREFUVheqtL5YpZVjVXp8DL0i0KyTaKhMtKYodc3DFZ3vmj+gWAjSiqSu+R3vn6FDW7u8e4cHL0UXLesGpxsayYkxLaUpyDOCHOXaUO5eLJlLDQRd9UmtSsMJCfo7D9uVUK1QLNbozgeHaHCkTGsHS5zKvt2ibprfl6Nn7TRAOyzqZics9GkNBJ0jd1KT3pfPUH+3NEdF5gGn0ELOoDSHW+enuLSHI7BuOUc4IfC+NIomPTBAwCISDYxZHyClFu+R0BOiwp3XS+h+D8MeP67LdLi+uXfcNkwCGbdh3xM85wU/a8/gVz2BGvQHwgi6L0eYWB+uk1PJsI+4T2cbxSLzYfq8DkKdUxMI+iTnDglx3HkfRvq9pHCkTUuhuvKS4n7ODAl6N7wu4UQKOuHz2HABFWQyeYLusyWi5LmGx/QOaJmf6s5SW853fVeHodMxnqg7vcPBm+BHm+AqQYczCtHyex8iuv1uoo2oPV/nygOzRAvmE+20vRD1XXeR5nO6YY30pCnunzpgPybc4bvnHLk60epRIebrC+OcCemXjXS16V4eaYmcK/OQRnNjQtBddCsu/+WCUk6Iub0IBB3Bgq48R+oRicf/8DuCUzgfauO1Yw50wcIe2MRcfCKrZLhzoQSJZQP6V1XKdN/4GPfiWdaV52DELr29/C6R5TkOR+7Nd7QVtJpuitnfZ4aAEfSZ4feEHD06Okr33nsvX2uXXXZJRMz1RjcFQVfyBoED40YJunMP+kilXhPCGNFQaU7hIpnOG4qdIbweL5RpsFylK9aO0SMFRIXQnTJNSzNFGkhXaEGmRn3pGm3V002Lu7u8kZtPw4MZLXkmy3dJ47DBSp02VIjuHBeDbLyI5kBVqqVKVKlW6Nq7kq1v+4OXg6AHWiEYBb4DszPwHhwa5Qj6tavW0q3rNrCRBeW4dW8vbTfQT1v15Gjned3SpResqy7rfgPXwQKWmqvSmvFxri1aPV7k5xgsybJkY2gsxJ5ZMdK7nMGF9XKxHNjuC+fTst5uJur4RIdjNB1iB4o6WiQ84pd5g1HIxoL7TtfT1BosGLXvv/bOtoV4I0FvxI4jzVhNTdck5/Bwmp8dxubqItLZy7SqWKZVpRI7clBzDuMRagp4adkyp5y7DqrSUVusBTUwuZaSSaCrB6tXKV1Hw5SaLNOEaBtq1F0zGCxDAuNea1q1qzs3mHO1rli2BB5vTo9HV2tefko95ULRcReoGzvqsmQp7ocf23yZNfHIuwHoLKYGI1FJe0DQ2W5rRtDj5H4q6RZjrGoUYwwCo3lE1Auoi0T1fzbu6e0h6ukmQq/LpYslvR2k/LEhoptXut8x7ZWgO+OSI+gbJYI+E4KOpf0kwhd16BZDrNFcQoojNwN0S3VhDKK2zzsmgyiuGlZseOsSYA5b2b8RwMmNfBkfPAIRieSmS3XaODpIQ+PDbDyNlMq0qG8+LRtYxOthIw1SzDqtB1BSG4/nIWJcoQtuvratOavzdcnSXalax7q/dSaW+NSacM6SSKfoeUtytEt/hnbqqdOyLmQupXmZtTWFGq1hsk60uohVDOr06DiaQNZpA3KnnUcpjIxHGQ4OPODhfRYBmfdrh7sVLBBRdrWdQhRkX8xRzFXUdSIdFAYrxgLkKxx/kG94x0I4Gguy+B0yQU+2zNpxh+1K9XKdCoUq3XTPIG0YKtGajUUqlmu0fEEXzevN0gufupB236qXenJSaqPLbSKSXq5AMjq+ACIAY9vJZhB0dIiHg4L7jXBNuhAvyExJiZe6e6Sqe4LuBibmWW9WHCw9WCYvRTQ0XqVR1MKjYWddCDqQ0ZR1iZ47p6OLwEZuVRl36ozE/SCCfuEv7k007k6dhKCrE7aZw4wdukoyp7HMNNtFM0LiUfU4ueaKplasvbDhmVtHPOxmzu/BKSt/Da25Rhd3R9Bnsg46IujITlK5JO/F/biLasYjCDp0u1ssIGqOFzhEwgfXdeRxGm0AKisUyO88l2NYeQLvvve/O1nfQNCRpl6SLKtb7ya6/hZZ5aNQFiKOJdXmDRBttzXRwvlEu6yQCLpTF6wnzvhEe/X7Kuvevs9e7MxTB6QElAQwyF/0ANlYLBCatMH+4hR4F9iQ8Sgp6khDZ4dfKs2kfu14keVMmW2OifRcnI9S3oMgwKLuLu4Ej0g6ZMJYGen2NZ/yjgxKdi5WUS+fooU9WZrfleWMJtFyYvdAh3FGAOZwKiUEvTDGdsqOfb20MJejp/YPMLFfVyhwt/pvXd+erGtlStg+yREwgp4cuzl55Ixq0HV9U5ciiBo+EEf2Jrp6RO5wyUaVpMZJ+qAYABB9WPKKP7VeMC2NgVaOV5ig/35jiVYWq1RPYXHLFHVVhihbK1AvlagrVWFyjogwRCCu25dFRN1Fk1wKK2px0Bl6EM2oahl6qIK1LSH0kdIIcjtG1WqZ7rgvWeriD1+2P6dONt+C/q9oUlcs0UipQnduGKT7B0doDVKzC0UWwEjXn5fP0pLuvOso7iI8MI5qkoKJVCkoB16mo1xhPLl2EpEfv6apqAc0+IGS7EXadibFzaQWdOdou/4+TotdzJ3wZf1LblDkasulzl2UjdSy67I68rsYPXKN0X+WInz4uvbXytRx96MXP48j6OHmoy6cJpkipMLi2daXylz2sA4EvVxhUj5UrnAd6zjW+HQ1kkxqGjqdukYsrDNF+UXRCxmP2gwuSreTlE/pAp3yzWDUU67RIY0cKFGHp1sazolTJJeS2jG834Vd+l7FuCuUy3T0Zcm6uLdM0IPoBUfDY7+zIaOkXV9CSMydpTNZBF2NypB8YmSo0ZWvSdOpbJEIlRvo3I7oeX+f/KixiPk5UiEaLBE9NuaWypMqDW0vwaD5FPdicoJ+hIugi2NRVw+YaOILoQOBS3OfBLxDlJCUauIQw1xstjl+7lMXZcRFaZcBzBONfMZe70UcUxgniHqPjm+k8eKQLHNVq9NA7wJa0LdIlhFTB6F7EZpRNJGeSwQ9KUHfdvnuXC8OAxEp93DgKflF5BVR2OcsytLOvWnavrtKS3IwRLH+eIpWj1dpbaFK60p1Wl2o01g1QxsrGZ6/g+hDgM7B/OgyIDWDRkhYPEfDoRikv3urHKhxmqlbjs03wtSSFuK1f5EeCqMZGVcoq4Esk/WJpXcHp/LHDGgQ9KGEBP2db9iNslgTnlPcR2hotEyPrhmn0UKVFvRmOVX9OSvm0c5Lepig471yx3tONa5JFF2zmHhCumWbqnVO2cfx+WyKervFIQgjHKiBoLNDhTuwE2eSgZBx8y/MTTdf5+Uh56QmGI0w141VabhQc7XnjpyzHpc5oxHqiNQqLYmcUd7BglU3ylW6+DftLdukeqItgu6GiqZrs76azDpTeahzNFyjPDbknJ9HhllL7NxlMKltwxmP6BsQOQ5wjyCccZmAe8b76c5LF/eZEHTUoOe7nPxx960OQ7VOlKDHyXnT7IMAM9x32kX9dZk0T9B1ijoirpBpHbxkUUYbzoOBhU+Meb23dRuJ1g1Kevvd97l+JTUh4iDoXIO+A9FAP9E2yzH3owylUjE5QX/5ihVMaL3jhBvaSdNZdZyBcKtOkAxSzTaUIAc78+EIhJxJp9m5igAN9sO8VGkXDk+vo+t1jqDDxtUIOuw07jlUr3MWJPTT2nH0M8KKHgiQ1Zmc9+exCg3sP7XX4JTMsJ2ahT2Sy9H6conuHhulvmyWVvT30YJsjnbv6+P7BkEfq5Tp29e3ZxNPNs3s+02DgBH0TYPjZjkLIuenn346XXHFFbR69WpW2OF2330ozGlvm0kXd26aU4dxVaVhrMlbloY7ECZIDe7CkjB5xCJTVOLaF9QGiwbU9Otelxbcn5G6XQgVKPVHxxBBr9E1wzVaDastnWeDbXR0NZWKw5SpjVG6VqT5XTmuzcFiESBPi/I9tKS7V+pLCQYkjBEQ9BRtrKRpnHK0LtUvkYUKDJYyFcqDVKmW6bGH2osEe5L50v0mpri79L6JprksKbRydJwj4Hes30h3rh/kZmtIU+I06XTWpUxJCJS97Gw4ImIl5FicHPLplRy7qt2SHGx0ScdxEHREcge6hDRu3dfDaVPb9/fS8t5ujiTBYNVIHdLYYYDjUwm6dk9Xgq4KfvyfXY0/dkN7URE8k2L3gxeBoEsEPU4qEFQDR1vtyh0eGSvS2lKJiTqWKENjJ5Qu6LrnbFzXsCwQ0j6lXh3KEZuWYUgtppr+MAbEy43xCoz63bJrvJyapr47hxNwlhRnOEWqbMhLF2NZAx2GB1LhsR6pEnnpDJ+h+fkc7TJ/HqeToSEdzjVeKtIxCQn6m48leghd3MtEgE+jMt6I1IERfOrfmhFyTWXXwcRjqknqe4Mh0UTUqNHOS+fAwYH6cqSlo0P7KNGyxUSLFhDNH5DIx5p1RI+vhuOMaD06umeJqt3uxCreoqxtSXFHDfoMCTqn+vn6WbccYpjq7uYV9sO7ROdvNOWBYwyRD0RN4CCLFqiKqJyPcjKG8q4nRNCDyFocRqWmKS7VSdFG1DkWi1QuradqeZAbw8GZ19W9kLq7F7v1z0UKRPGYyIqOc4mZEPQV2z+V18MGDsNFLO8j5SjY4NwCudt3QYq2707RNvkKLcqCVKJEBxH0Cq0dr3iCXodMT/dQAc0m2QmHMiQ0agMZdC4erE3OJVNuELg66igXJkLPVWrz/rK0JOaGW2LSzQN2eaSw/FqG56pE/6UWHT0h1HDG7pAfWt6gVwFBH1xzc6Io8PFv2I1lHWrK120s0lihSvc8BqJeYQcqHKV7bttHW8/vor48HAjyXbTWsThK4RjCknDcCA6d1cvoAp+i+b1Z6slnaMlAjh3kGHjAQqOh4yiRqhPrVTwvMlbw7vKIrqGWtwfOxMhBtAqrnRRq7NyGgxSEX1awj8i5EnDoNOmBoOSz0ZGLe0Gt/aVXbH6CrtFvJuj4aeIcc5OlgSDqHHUqomFaxuWhd0xOZ25pNNlFmFFTDicl13fz+5MIsd4PPkFQmaAHy6zNlKDnlKA7H7U+I8tA7RkUpLrr9zy1476xgFjzPHG6D9kXPOdUtvkaa80oih5THGBiu/jTw9cHU885NLRh4wOPEj34KNGaDUSr1rjeRrgOCHo30dJFRHvsRtTdJXqFj3fZYzMh6HssXuoIui7biGCHZNRJiaAsm8ZLpnFfIImi8w//G1HurJDiFI7LsJ33+OiYlNLECHqD3nAuStgPKEuETY0Gl5AF2hR4oEs6tq8ZK9FwqUJDxSphjvfx8paw+SQjhqP1XB6TowXdXWyrLurtobXlEt0xMkwDuSzt2j9A83NZ2rm7lwefEvTv3GAEfbop/kT+3Qj6E4l2m9c6/PDD6eqrr6a3ve1ttPXWWwfNJeRE73//+9s8Y0SULnzFK3jiTowjNT+lkhUQx7XFEq0tlDmSiygESM5C1zEcqZdIPR7jNDl0jpRcT605yrtu1wuyYiQgCiMEvUJDlTpdN1qjNRVEVzKsSEZGV1GxOEypyhhRDZ0rc9x9XmqJ6zQvl6eFcBfzJuQJJKpUz9BIPUfFVJ4G0wMc0YFxU6uVqVjeyBH0VUkJ+oufNSGC7iO0gXkAo1Ip9WCxwk6Nh9GReRhdOGW5H3jNi7y0iTQV4dREn4YrnntEZ5lUgpg5ZeZyqHlMINIHQS7100jPdmnaOUnPxtJiUBoLu3I0P49UqBQ3L+JacxdtkXpFSY0MDS8dH/p8qAX/4LV3tWWw4s0oQf/hi7HMmiRi6aa1bNyopF7nLv7r/1mv/RAIOpZTgzOo4pwVntineMk1OAy4KVwGjZPS3IVVjXkxuqUrPDI3uDkcZ3BIRI2bSKFTKkffZQM9wHHssMD4rVRYKXOqM8o56m69YkfotbZVnwXvAc83L5+nnQf62Vu9tKebr4vcxnf8NlkEnQn6RjHuuAa9SdTbe28cOdGUaq05xz2G6e+epAffN0TcJ5EuDUaWM+iySE/HMlAFiZxnYHzViAb6JL1dDcShMaINo0TlLFEBy+MguuTWR1dD24e/tAZ9hgT9rXvt62rQNSUxirBFmQCS0ylpmtL9u+f/s/cm0JblV3nfvvO977655uqq6lGtVrckNEOQWmAhAVJilhO87GUHE1sCHEY7zCRgGUIAA8sRyxAGIRJAyEDMUsgyKIBgyWAxaB67JbXUXV3z8OrNdz7n3PDbwznnvW5pcV9VSySrrnT7DXXfHf7nnP3f3/6+/W0vvADakbnDMFgyVpy9wZTvl7OzLFFnyEeTlWi4PUySzjxnjayIcG13V7aGQ6mk21JJd6XbbMp8synN5qI02yteDHJovk9yH4A9XkpjYZrKWz92MIn7Pace0Pj0dACd3YMY/vwFkZOtqZxspLJSp/hB32ZVdibMHE9lc5LJ2iiTWq0p9UZbwfikYmO88AihyLY7sR5pZlpEz6Q3NZuqIr8+i5VTMOolEyTfoeKykW52gUS85PpDHaMThb14rLOClWm2GJL7D/jfavzOUtk8IED/1n9wv07RQJ20209U8n1pbai93hQKiTmnV9uyAtB21VMAd7t2fQa5Fk4N3CGPR4ZOnOk0qwrsjy01dT46YF3d6n2kHSCbz7c1YU+ZSn9sjDpeGSTyC86ga++7zn5OFaBTKEU1gWRe1XAlaXsxVs0u/M8O0G0Kwe/9yRMz7RX7GXSdg54rxewkiMsvj2t+OTIhQnuq9ytYQq69j8HVT1Bm0EvxLoqawf7GP+VXvn8TQH5PqPSJH5rfANCb9r7iGKrXxr79LwB6mMTdDEBH4t7woqfGJ3f3L8v/owc9ijnxtbwvh0Q+L1ZEEdOvK0Q85je0tzUpetFDVVVcu7b/5PXXYND9ecu954+fF9neFdnYiikC7l/SETlyWOShB6wdYK7l8np/sfFNjFl7wbETOYNOnCdaHOm0tMhGngB7TawgP4NJRz1KbFW5O4X8aaatcea2Ti5Wk43RSPM9rsNoFyyfS3mBnTVA3eI96OQVmptUUVPaUWEfgFS4MRzL7oRrdSK9caotTwrQvehnJNVUJ/kgl19oNeVItyvXRkP56PaW5j4nW22Zr9XkZBNzOXObpwj9m4/Ontt9ljTh9q9vwQrcBui3YBGfqadYXl6W3/u935OXv/zlt+wlygw6QLfMCOVp0D7UHskePTj07l3oD+Vib6iJFZXE1VZDTnSaKqnBwGg0rcgmgFgBOpCnNE+bERUicqSFe7bIgk0/kkvI65KpfHRYkXX6U3FiJ3DsXpbhcFuyZCjTdCwNkryqubCn04l0qjXpOmsCkA0GJK00ZFKdk0mlKb36oiaNKgnPJpJONiRNx3Lj3GzBKAeZr3qhAq/yLZdpO8Ma/FbQkuGZi9wdoL45pMgxVsO3q/2xBsirvb7KaQmUgEn6JqnCAhZIMA8hY9IeJ3MdJvvkcYxtAxTCjvO+DKCbKRKbCikv0klDSQYyFZh7YqZ9+wrMixvHnA2Kfk3De/aJAOj//V/dhIv7qx9WljskYzynGQdiHAUITuUT2z25OhzJ2f5Iro0m2msPGOedcDfTp5r000QLHQCoeq2uAB05V8wi1d5TnxG6xLzQKsCLAoWPAYQBdMY0jMMYaYfxFmwpmy8tCr3ECgTKovumHEA+rg2SDsCCMuj1uhoZnp7vykqrJc9ZWdHXXahW5VsPOAe9DNDprogkiJV7ioy9DNDj+88CzvO/1SdyFj1UGvup2NL5wbmSsyIAqB5N9vRAoLE11hxwbkdW5Mp1Y0OSmsgYWe2CSOtQwdprrhaZ9S1m0P+xAnRzIg/ptOU8zpQoADF1hCohKOC4Sy/GPLDpJGODxMwaOQ/ic3FJkdxaHdLl8zGKyh/1dOCdf4rkPxLZ6Ad9cmtTrvf6MlcZSrsylhWMHtttaTTnpdGku98P1h5myyNOQaTn1zPn8W8cEKDfdeoBYfoXPdGwNjmDrrJU63m8v5PKsUYmxxuZLNdYQxszYIlipkCdIhsyy267LfV6Q9rNlmxPKMxidjmVS/1EpzH0Mo1Wanymh0hVY9OYJqfsd8RWA+iZAkkS2QDo6hrt1SlrhULWbUXJIh7b8+c96CGP1+skjAJRGaWyce1gDPq3/f37pdGwYqQZb4r0+hOVvCejVONvJNbEdyvuWXtNtco6FnQjfzMY02aQyRazCb2JYr5Vk9MrLR29dmTRGLZWC6WAASHO1B1l4UW2h/StTqXrbButJ8RC4ivFiqv9TLZGAA0zpFODvpAelwzEoic9im7KsPv+UTDsBtD/4D8dHKDHmDX1P/HNScGmxwfEUlqA8aL1aGzy8XIYcX6g2NvKSpbSmLD916gysqXrKy9U+4UfofFpAbrP+ia/QaHQAaDjwu93JlaUX4+X4vkpjADQTyyJvOUmxqz98E8a06zHzt9otCVorHKlml4/4U/gfehRcIwFi88dn1OvJWfOKfJoETz2iwDc/prMPfetPSKW/lwK73t+3x+KDEcinzor8thZkbH3onO8CbkYjLa7IsePiTz/eaYkCzafc4HnZlb6vz2gi/srTt+ZK/CIc+QPz1pelKUWpm1WwCP2o2iBcbbRuYxes7hIXgXpxd4fAH1tOJQnNncMoPtBNxKp2Ezzuegucac1TqXy5Ca022mOQjHOnndDc6JE1vojldDD2sd+xWO1RXGK9B0z0bbuHSfn5+XScCDvXb+hxcF5WvJwjPd9sZcm2vbzzieenKmgVs6Bb39/61fgNkC/9Wt6y57x7rvvlt///d+X5zznObfsOcsAva1j1vaE4CKjfpp/sTmQiVzsjeRif2iJ1FRUKnOkbXIcGEwkjJsYBSmTbtykBXESrUzHUR1B0letyFLDKoeXB5j2TOXRkQH0gTqxZ7LduyrD0Y5Mk4lM00QaNYBWQ8Ypo8WGaoxm7tk4ageYFJlWW5LW5yWtNmVUXzJ2hqQDgD7ekCwdyY3zs/VS52PWvuyLVPIUm7TtSyHxs2nGJt82b2ASvmDSYWKRJQHI14djBevro0RB+dZ45H3gtjnEHEzAKM7gSw0+Y8lN3MHlfAsAj4qhYaA8+rHdYTRG9ig8cYdg2iWC/YiTS4sbexJUW1fbnO3Tksy94c9nS1j5u6IH/WFl/EMqrueGLyQMOkzP2f5Abowm8thOX8eprY+YI59Y8UXsHIOZUgY9yVTiXmXkmTLoxoyzwRlAN3UBRQ7WkDW1MzLaBsou0sXooQF9wMh6x7YZIk/jfLTChqeA7smgSamXwnkP5i9Qk0Ottp4nd8zPa586bR3/+s8ONtoPgI6Le86gB+W/D6B7q2qRXO6TrecJZQmMO9zLJe7lnz9X4FHDoMSYcgA6zLnOVMa1HQOqusjAk66tvsgW4L0tUmHs2pxIY2Hvsweb9RQGHZO4m5C4/6OHAOhFp3ehdimANccbJkRdv33EDUwISghAekgYeQwgPVQm4bOhIEVbgMKQsOh3D/AS6p5Igv0Q+Nkoeo73J5k8ubWlAL1TnUi7MpGj3Tk5MteRZqMjzeb8nkX7bL3nhfTdGPS3ffwDMyVecb2ePvksZV3jvLcanxUiUJRwHT+nm8kJGPRmRQ7VkZs3pVZlzczzo5dMZHcytmuUsUR1Zn+3pZ+KrI1wd8/k3C7eEiIbE8Z7Uesx5tY816dSn9onipYbcySHcZ/qaDpiZB1ncxJpPdaFfV5lasl1EbFdts3+hdFpRgHT42pcIHrdANAzWb86W7yLtfuWr71famrSVEzGGI8zdWzPkkzX0UZJ+mhJNVM188ky8OT74TjT3nVGsQHQdfxmlqnUfdWl7scWm9LCQHCubgZRbI6VikDYsk8D8AldFAEATxFCtkapnnc3hpkWTUJOb+dpIXHX1is/x60oFaM49577cYIC0N/5ZwcH6OriPjRwpued7xPBoOtnKM3SHk1sjKNei3EvAftAg+XrjgcHeM0vrIiZpQLlfgCvsa+oae5troY5r5n3BoeAu3rg+IjB8I4Je0etw6i5n/WgM2btV24CoP/IT4m0UC2VlATRW25tPkURwzwOfMza/rXYZxQXBdk4d3yIQAHQHXn7VqjnmH6/b630fXnxqVzoYO751o7I2QsiyNxVETERyVLzIqHoML8kcuSoyEMPFQCda5fX4jYairzpBw9mEvfld95pWarK0TMt1j57ZUm9ZIJBDwXV1X5fNkcjlbD3xjZFh/iPUpFcQ3MRwPRwrCMyAcyhjCyP+gyFZCTexKllpOzkEXXLYcgnNL/1Fh3YcwrGOMT3xuTExTx2vlfCRabq1XR0rqPjOTEkvjIcyvs3bmj8qaVGlC24QmboPit/fuHiTPvELQMmt5/oaVfgNkD/W3xivPWtb5Xf/d3f1Xnncwy2vAW3nAV+3etUKm5bsIXJ8iZUKvD5q05lm9FW44lc7o/k8mCs7CpBAwZ7sYExkBmY9acV2VCATg+4AVSVGKssOFODriOtigLrw01jRa8NkcuLfGZclc1UpDdNZDxNZae/JsPxrluNijLogPT+ZFv6kx2pSSo1Saznp2KSfRL9Sq0ttdaSAvWssShTGB2daTmRbLyuAH39/MFGwPwfr3hoD0DXNNITlnwUkbL5JH0WXG1TNLkS6wTwA6SzESDT1s3e51ZGHyLgjuNjcqtpMcYLOacbI7G2FFpISnk00n8SIzOWqvrmy+ah8gF7PjUgsiNMLyHsohqbqPTRejhho5V9ymen2GkAQP9H75ot2efvCon7K6VTc7DkJ5zKtfXDmxvxxtgY64+ub8uTuz05u9OXS14QgtVpasXYvA40gfQCiG1m9hmYta6jz3yTU2CuI9BsJ2cNokig40187ZHB8jw4X3MsVcbmm3VspgB9BWwua+Pfg8W3NTQGK1zkWV/tY6uK/NqHPjzTBhjrlgP0ic9BL2WZ8a1bEhRMeAmE57gjflfOjPREKNjs/KmLiV955NGzxj0lWLd011zaa0ORWmoO7Ux/3NkU11/4AAAgAElEQVQS6fdFrq2JYPozpSeyJdJZFplb5foUqbp5Ut5u7BllDtRd4q4u7jcB0P/hgy/Uc9w/on8WW4Awjrv21yMtd8djTcZIsEhsMOsBnAPSrZWh5uNyLBmL/kOOvzHpBsr13EIBlBsvGm+vscFly+Eroe+B82kq8sR2T+WLZzd35GpvIHO1qbRrUzmz0JXTi11p1lEpRTuPRoxCPbFvbyh7BQPQf/OR2a7ZOO+OH7tHYwpXzVLdDBQZvU74YKoCipovWqrIqU5F7uzU5UgTQ6KuNBpzMs1Geh8nIxkmA2VpBwljLhuy1O5orzpeIVvjqXx6G0NIkQtDUSYdjwKiYqVCoizSRZ4+FeuDx6SUqM/ECx+lTjGFGeEt3NBrjMdjhBpxjqKs7THqyOxxmthRgBYz18QwTd3QPaYA0LMslbXLszkbx9r987/3LEUROeNcMlOExQpHbDPpLIZUKxDSzw6At750+tcxmRsA0HcntgbQ4uqvwZrW5PhSU5n0Y0stabdqcmS5KQ3anBrEvpCG23Pys3kLTOXSTiKbIwPnFE1y1rkE5gLY2TleAPUycx6MepyKSZLJf/qLsweKdz/yvSJPXjOADvusha/SqDBeIx/f5SAQgM402jI457oyqU9x2Zs6zG5Pq27Zg+Cf/nFqlFae/11uOcKbpG4u+bT61B2c93hvtPTVC3WDfg5fZxQ0yLZh0G8GoP/oT4u0SBf9mJdVPmVwrgx6uLiXGHS7Rp7+pmC47NoeEnffF/K9yCcoRJtVuU0lFj/2knilJ5l5fsVGql1f93QFcM5x/Wvzt7kFkZUjIocOi9z37L0APZRgSNz/3b86GEB/zT136X6v7YaJEQIPri7LarutYDeAMr+/sLMra4OB3BgMFaj7WaJsNrHIPGncJK431OI+7LtdO1bJMKd3IxN0H/A8A+NgNYF0WT25DjlktOTolIbpVFV+SO6jwKQGmDpWjaKl6F6m/kOdjty5uCjXhgP58MaGmpDu9Ica11ueB+p8oyyTR9auz3S9fpbT5Pavb9EK3Abot2ghn4mneeELX6jj1djA77rrLmkooC5uH/jAB2Z+2b0AvSzTjuTlqU9pbNFUZdlUDK8PJtqHDvhs1Oraz0YvufYSwhAjcc9qOqql50EIZkoZTXX+rciRZkXNsw43jUFYH2HaA0CvyFZake00keE0ld3BuownfWPp6NGpAHhr0ptsSX+8rQC9XkmkVqlLo2LzYqkIVxWgL0ul2pJKc1Fn+TI2xhj0G5IlY1m/MJvZWazdL3/pc2QeiXup/1OZaTcVi1FEOurCzcssaTDGBoCOQRHzNAmmJO4239bcxwGYKnfU5A5zH2ta42ixVmwUugHUTIqJ86j2UWvCwDgkA4+TlCSUinCw+s71OwuyB0DuaROIEWHF19iwAc7/5E9nA5m8970APUR9XiRwOSveBSTlNxgrlaTyic0dudAbyIXeUK4ORrlk00ZgoWmzzMskqVFtNqmoqg9887Pec1MCaJ+pMkpGFxhQMml69KQD7JkEwGOivyzAEEoFnisk+mb+A0A3Vj9nLUq9yrwGoJ5Z6//+Iwdj454OoO+Rp9uOX7DgJRYoGI2QfOQsuzMZZaz+2cYJ7QH4yJsn1nOewYonIqstZOEihCjA99WrIptbIjsDS7LrXZFaVwQCGJV2/h6s66JIlp3R8QymmIN+EwD9HzznhaqyiF5wTYq9UBNGP9d6PWV5D7fbstRsyh3zXQXpi02TLGoBzOWDYZZpyS7X/V7mfL+fg/b4eqHNkmUD9LHuBtCncmF3qC0V55j2QALl9mknunNyfL4jDUZK1m0P0L/3J7DDXvohJKceygHov/XIB2dKvOJ6PXrsbmnDUFdETrUbGrNp80AqfX4wkd00kxcs1+RUpyp3zdXlSKsm9XpXavW2TKncZGOZpGMZJ0MZJrSO4ECOJ0ZLpdSDpCI7qAZ6MOhm7MmINgA6/46qhk/WdVNRRhWynluTivVK+1hKDJSYYdxumDM5RqEAdMYQcY810oKMY2EVijs4AuwBXHngNC9KEj9TuXHpYAD99X/3PqmUY0JcomVUqDVTQ0R6TP17i2xmaMn+Ohxl0hsk6oze52vC2NBE2TDifatekaMLTe1DX11oqMv/CpJ3xsu1rPgK287XDkqsGkVIk76f357IxiCV7UR0gkacXznbWpK3s3cZsHNn9/zrXkad9w9Af/d7ZpPMxnn3o98n8uRVEXrQka6risMBehRWwkWcXmj2zgmMq0vc84JCac63qVzsoohrJy9MRtrjcTO/OP330QcfYbbcX63HysQKObCFNUfiXsWbIzF5O9J26GYG1Oga+pOViwWdlsjJZZFfvIk56MqgO58T7TcBzEM1F6MLw1iPx5WHVOTgOYKUr4N+1n0APfdDKRUsjJxw0mE/2vd9Kgo68BOw5I+fEzl/yfYNZp/Hhp/QujASWVwWOXaHyPKqyBnGq3mLRvktwqD/wg8fDKB/9T136yxwJOzDlGxE5MzCgpoSm0mctQ6ST6wPh0qymMx9ko9bA0zDorOW0T6DGbCZxJmpHDlFFArJXXhO+wzWYkU7nKpRnSzQsW16whohEca1quxz0G/7geVBNq2zIoc6Ld3HVlptuWNhXm6MRvLJLfLmsdzY7as6qB7eQ15Afmx9baZ9YmYAcvsPZlqB2wB9puX6/D74h3/4hz/nC77xjW+c+Q3tB+hPW0HOn9USFgIWSSU9L/S/0FOIezuJFqAEtkLHAjHfFvnNtCLbWVV70HcBy5ocMK+8KqtNHIkrcphZ3VVRgI4kFvYc5uSJYVXHo12fJNJndvl4RybpSPBtBzjREwjg7I03pD/elLqk0lCWBSlz3RIuwHy9I/XWisot280FC2xUlLOJJKN1SZOJrF88mMPs//bS+3UOugE+Y8pD3orsmqRfA6XPxOQxGn4xJ3KADqPN92xigDcCchQx+F77KWtVXduN4cCAfcLnpBcJCamzZ/63Wql26mM4oQpsc3C1WOF9esYSW2+5mSIZ0LWv9h7Dv9RG65RMtTSxpQCTyDf9xaMzB/FC4v5KZbXZjGIaOlAENmyLHu9sKldHJv1/cncg14djlbvTq0/CjYu7yVdjHjPnD+wmzqluAudGbaFI0NTRT/SQ28UpDkPGzVoDUILYeaotGaWd35hWWJEwD7OCgPW68ro1NRWjok2rgjFcNh6P9gXO8blqRX7nkdnWbg+DvmUSd4QVAaTLyUne2+e/jKTRxQlFrIhkMgx7/LzQ85lW7Ug0fQROzooAYgDgHL0t6zWXvkhtKvL8Z4scXhE5f9mMfR79tMj5K9Zr3pwT6a4ac25W+QbK86EUIUMNrOLGTcoAMQN3QyS5CYD+3zz7i4wZ9YIMCxFmgvSVk/AoQB+P5dT8vBzutNWB/7Sb/HXpI94zoq20lN47HAWMvY+LnnRTHZR9KsqBW4HDX5t+bvh82wu7MDPIJy35w+SHQkGcz3aZF1lvtH4E8LCCTHFmkMT99gEB+qFjd8rRekXmayIvW24J5p6bg7HO5v3ELuMPM3nhSlNOd2ty13xTjrbrktFeRGFUJlKZJhZz07G6j++OUB/g8m6MED/Dwq/RwoKss9XRdqTdtCLDbCpXBsYWLdRt4kerasqX8z0midjoNvqll9sdNWiycWXuKSC0IyAdNik8+Cikpjp2zFVNCuCJh8RF4i4XK5GQCSPE34sHA+j/8KvvVU+MAHU8q+HxqaR+jZXnSsf3xjYaw66TOfg72O5JKhWl4DI1nNvuG6N+fXtk5lJzzry1UbZVZa5DsaQq9Kkjlz222JC5ZlVWF+oqhR9lJPsij29MZK0HQJ+qcZ/uVQHKla0rZNF7GPToPQ8TOS0yFBczMfD9Hzg/014R8e7ffL8IEndMJZE9w4zrFMbwfJhaHGQr6XZMGh6AT8GoM8Pa+z2xuAkQpEc9rrcoXCroLI3q0pAem5NfqIB/VeZ5MCS2qorNY2lM1uA1AGdVN8nMeN+jQkJea3h/uM+tJ8bx2sRd8qFuW+TUqsibfnk2kMnbjLV7o/egR0FC16wk+c8VEiV3/jjGRRzbq6oqx6vYD3IFQ9QG/dDrmpQY9Chm5CGrpITgdyittPf80yJnz4vOMuduF6x9PxyIHD4mcu+zReYWRVZP2NpHe1ywyKO+yK/+xGxrF+v2d++7Zw9A51yOkWnaNujkiPr6uCK0cHA3Hx/yAKZuoDSlhdFgt40q5LHkMLD0/KxAXdcp8jEjCWwcpP2N9pO7afB8A5M4y7fJHfs+8m3P8UThoO0+VTnW7crdS4uqCDvUbquXDo7yW6ORnNvYMkVXtGtpATmTc5sbM12vMwOQ238w0wrcBugzLdf/9x+cj7tC4v4Uo7On/3xIc+i7JOBsjnCvtKRKpbs+K5jKIyNz1sdT7UHfYTPEFCebqlwa8ALzewwzOZjzhkncDzlAH3gi9cRwqgCdWehqGjYZaHIHeOKepiNN+AajLRmMt6ReyfQOQIdZVz6UxKbWkVprRSXxc615/T3hMsvGMg6Afulg8ruff9mzVX7ETQ0+nJHldenlUXbBd/IY2xTMagD0SOQVmCtALgA6Ek2el+QOqfVav6/BHKaEV0PSzr+TIbBpENB10/ANEoYrxaFYZ5hTBDB2Xlli3Vjs+QH79IWGkZIlOAZ+zTTL2b7or5WpSs+/5a9mG09XTh7e9upXqjGSrp337ve8/+46/VxpKteGI9lOUm2loP8ccE5RSBkxALpnslHlZ42bVZsRqr2obpAXo8/KQLssS1Sg6fRtAHRGkKjTu4LvILds82WzNIM6H6vmle1QPVCEAgztTMY2wzhxj2kYdCTutar8ygdnUx/8jQB6GaWXpJbBdBiws+SyzLrCSIWJUJkVCUTB0+IXQUZL4qk/O0Cv7IpUGPmGeU9F5N47beQNLAij1J64JHJl3STtrQWRzqJIG38zZSg/vwD9v3728y0pD5M44oOzpDizTyiqjGB4E4GtXm215Z6lBbmjO6/FJPwErAjJ8TS4ZcfcWI5us+hu5l+tuFWAlThPA6A/XZTlMRhIYrrJGB2d8jBCsYQxpnkrRBGw/DwBxkMZoixKiVHnsXy+3370QzMlXnHerR67W47Up2rm+bLFhqzWK8rAjP9aSfOZPuZvU3lwuSEnOzU51a3L4XZNfT/SaoOp3aqJSTDXSybKnmNChuyc9psA6BTdGMdJQRITOaLcLkahmcjlvs3w7tZRoTAL2MamAdz7CfEIM1KmMdD6hKGmjStjJCWH2B5j4xkp/iE1HVMcnRoDr6Ov9CJg3Ryl+cBmc78GoM82eijW7mtfc4/UoHdd7UDsKQzMClNOi0mugQhlhY+GVBDosjD61gHoVT4HSrVhIsNxKuu7NJOJzkImvgO+1VyqheQVt3eUblU51GUsW0UOKcNelSp7WKUil3bMvf3GGHdq8yaxuxV4lV0NMzF3btc4zPdu4BbgmL56A/hTZfc/8MELBzrvfux7RT51XmRr1wF6aXa41xAEtpl4ND9nRmz7QWT0VyN9BwAC1gHawSYrMPeYWC5m6tnwdAC9xATH41X1RpFdvRcsTuqaIMt3eXaKkGRqLu55/HNfACU8A6DXDKCfOSLyMzfBoP+Qu7gHaOVrqEW0yFGS9gejHqqEWMOIWeU9I/6tXLCN7y0q2i1fU9+LcruUUC/4L4K9hzHf7YmcPSdy6ZKx5Uja9U1PTBVBkeXoCZFnPSjSnhdZOGzrHkabAf7527ceEKD/V/feI5Op5W/KTAv7trWHWHytaKGL/II9QZV0fq6HN4k+qlJRdn19MMr3C9t/YmqEzTXnb6woTjshCh5jyrlWtZhXUqtw1Dp1fJ6Im6aQhOXne1v7YpOvoobkeu905NTCgqoJMTolL0SOvzsay8XNbQP6FAvyVq9Mzm3dBuh/m1DebYD+t+lofB7eSxmgAygIHBFYY/+x4FwkEEh0SGS3xqnsQkmUpu/y2D6gJE01qWJsDszHDs6wVPmyVAH6XLMlMFF3zXcUqByiX7BakZUmfcEmbYbReGKQymYylU/vpLI+hgFBapx6j/BUkvG2JElPxuNdGY977iRsQJnXCe1stQ5AX5VWrSFLLeakV1QynwDuB+s6Zm3jysGShzd/yYM6U7ecjEd/KbJBWJqQlgXYCXAQAN1YXWYJMye7kGMDorXXiQSrXlPn6As7O8rMMveS51XzIt0ZKX5U5NjcXL5hENphLgDYSGQtsAMqbeRaSLRCMgVMzqvAatJm1d3opVegrgHcNoLBX48Y+e73z6Y82APQX/NKVR8ES8RzM1INtcRjO7tyYzxW1hyJO/N7e4ndMdfTWvTUelJV0ghLrY73JKJmQNVutXXDs34x2+zUedbZ7nCc1ZYAVxPwlc2Wx9g80YJB5zVYC4oDKoeOxEopbGPRQ4oGYKMKPU4YEmhzotnIj7Tber4sNGryA+/6swMlrCpx38+g+4UbjEUkLHulzw7MndHQpedQkqhWRJIw8vFkqu5ggueCSJyHmaIneNeSTBJiPludUWpTkRXc2Jsizaa9zsc/JXIZaWrGuSIyf0Sks+IMlfech5w3kvrQhUbimDPrt4hB/5r7n6eJTzASvE70j8OaMzkBaTHLCXuOpB1pINdVsGwAZZiHuFHcXGq2VP5Inx/xp8xqW0FubzNnxFctruU3e93wAgGsETdJ5q8PhrI+GsvWhNjLWEub8BDPE8+CAkadv10VYzUVexTHDID+Hz5xMIC+cuxeWalNZb46lZd2p7JKX3zFxoRdm1RlmFbkjrm6LAP8OhVZbIqkKJkisRXrlYRR2hlP5UafIhsxKgB6qj3jC20zRVqew0ekooaho7Qil/qAeRQumUq9j87xWWHXTXUDiw7QH3pBcrmFSVNNx0kiFdV2rAGFP0xITYEzmqYK6jk/MebqJ/DVVUkrNExhUOcFFi0kpbJ1ebaCZOyxr/3yu7XoUL5FoTYMz6I1IYBG0Spt/2K+LVO91mrat24Anc2DY4Cqqj+cqOEc89b1qLvsXH/SvN1UXvToI20/vFCXuVZNji+3pNuqy0iLFCLndxO5PiSmuvrKwZy6uqsngLGACjbztg5/UPhmmiDJPDrSTD784YPtsT/0L0Q+8IgIxmHEHkCajpf07ijWZmVZpDsnMt8V6bSNTeWuzDoqIwffyqDDYjONgD5w/zxlGbZv5wXIdNrX65Mmnfc2BAVE/tzxnpjJTWwM9h4Hcu46lx2grpMQRHoDkY1NW0/Oa56n5lM5qJcsdETuPSXys2+djQUu77Hf/+MiMQc9YirHcH//uad5Hn1KZnmeFOpnjM9qKDDSGTuv/MTOmfF9f1f0e5X+zlVa/D3rQ8Hk4kWRtXWRjXWR7S2RUU9k6K1TdMloQbcicuKUyHO+SKTeFqnPl9qkLHxaz30i8ts/OdvaxfX6VXffVfSD+4fK8yVXJcVknJU2+SyqPcsBUDohOce0jb1CAfpwZEqUWCcHwkjdg0E3gG5qRogWPgS5RlltkBdf/QBoTqxKTWO9tSUSkgeiBm+eWkVSFDSYnLZahWqSKMdYX4yJd3q6LxTyfMvxbgP0PeH6C/7DbYD+BT8En/0NADjLUsX9j0zdXGyWj5A7kb/udSo733+LSl8h1YT1MKkl1XVMwsJwJ8xYSH62EkD6VK6NLWECtKtpHH0uNWR2LU2Y7p1HiliVFUyXqhVZgO3AOEUdeUXODQ2YPbqd6uxcGCUClrHkmSTjLUmTXZmM+3p3nKrSRJP9OnBSifuqGsotNjsaJEfakziR4XBDJe4bVw+WPPzSFz+kzJrJAI1JCEMolbgH41AKzJrYeIKj/c+Kr+ntrOUAXc3hPNlWs7Gazd281u/pOuw4QDdTtKlkuiYVBRUAUp5LpVFugBbj1XivAc6jSkvSZskijzeDEJVgeeU4ZFXWv+5sIMZCOgd9tt79cvLwG695pRZqYr/nPNtEKZFlcna3LxuTsVxzd/vrIzY7GGncrQuArswlRlAO0NUtH5NAxje154xBd5mYApeQkfnIJd0UnWnUBFjn1rq8TPvbnUEPYy8vWJhEDYdUblWV5OrYOwf7KrfTJM6MEBeR3NZqcrRjY/BQjPzLP37XwQE6Lu6YxCmLWEg6g5YL9+e4piPBAiNwJ3HFXR1mR6WDU2sryYGjM+aaNLp8cA9AV28HS8yY4gOAhzVvlhLTTz9pyda4YSPVkLW3l3KkYeuzj3XLAbrj2VsN0F9330OazZXl59YPSE+0u7e7kZ/OjcUkrjOnM2QjtcSJfGc8zs3/dKRks6lJ2tFO20uW+6Hz3p8DNCs/UsboDuRDCqrM0lTkymAga8ORbIxNuUQsiBFvwZzHSMHwpeD6jlvEchKx3/nEwZQbAPSFaiZzlam8uDuVQ7DpFaZMTGV9bED5ULMi3ToAXWShhfKnIbU6DLqdK7RDYTC6OUx11rb5YlQVsKDG4hpdmatpTF2Zo8e+IiiqmL9+pW+9nDV9TQA6ElJivQ06hB2noLEzZgRiJkutunR93KcC9P5Y77DngHik5WkF9opJIfYaO4A2DOEqDb22Bxqn2S+Ii6lcv3iwtpTXPHyX1Fwt9JR91i/Ogo0spknYPxU/6ygplFC+Z+gMdTfuQ6I6GtvIthRneGW9TTkVbtFhA6sFSfbcTk0Z9DtWWrLQqRuTXq3I5X4q6yOuCWS4tgbc2c9svrjFPwW4+wG6G7m5ht8kvWkmHzogQP/Bbxd538cNoPf6BnCJM1o4dMb50IoB9DmXuPN7lltjl1uU8JlD5q7y8wDLzsjnTLinQvkVWwKjWtALJ3kH6TGGLaTtxGTia6gPkOSrLN9fDyAKIOWzrAdAN64jb1fivS7MiTzrtMgv/fZsILO8x37fj4vgJZkz6Nra4X3vpZphsOSBb/O4EYvwdMlhaSqI7kElNj4PaRHeSoyP/pv/rEULrr+hydevXBXZ2DBj0d6uyHBXZLTrTfq0FgDQ6wbQH3yBSK0lUnMTvNxwzgvLGMr9zk/NtnaRE7/qztPaKhdFGd4yuUaokvgZsoMYC0Cf17nkBq4pnEYhEvNfvidvDmVC5FBcN0Z+WO4YDLr55ZiCR93eQ3XD1Brte8fHwHvYHaCHKSn5DjlIo1GXRr0uSa0iiRI8eHKw4dvio8Th000mifQwrmOk74RJG+7LkaVy9rbEfRY49Yw/9jZAf8aX+OAvgIN7+TaZTOSDH/ygurrTn/6GN7xh5iePYPRrr8XF3Rj0IjBbCmmsNSZj9hXmiP6VRA13ADNVY71JdoTeYZG1hIQtlQuDibmEhyZW+3bqcrjb1v7eBxY70qlWZV4DWwRAQ0nsV1cBZPSu3Ujk8iBVloPkoF5B5j6RbLIh2WRH0slYUsw5NNhXJKNfsGK931XkzvWONOlBx1ip2tAKLOxJNk0kHW0ag37t0oHA0s++9EGdr81aGXNujAFBl0QwkhorYLizM47Cwcw4KCRRb9I/LcZ+GdCzfvT4SqA2ozIckAuTEZ4Ll3tY9BglpgZ6gFAP/PVauBMXz2mybzMTiT7DXNquzH+IMB2alKrobBL0tn/TX87GKJWTh1979StlQSXBwRzSCmHMzDZj/NJMLg0GsjmeyCc2d+VibyBrg5EaFBqjZIoAM2LhHIXVNjat2WrJwvxi4UpfYtCjFSFK03b0rNeTWzDrAc5zebw7ZUcPeng6ad+vM+2AcarP9KviAr9Qr2vP8F3z8/peV5sNm6E6Gcs/+3/eeaBzrmwSR6JKEj8ZGGMw7qkXlyWnuVtzIQGkPxOsCcs0B7Im2ZyYedvajiVLJHD8PfgoetZJQjF+0z7BniWbJJochwDmrba9zhbs/shNnSYinaMizSVjO0iocgms94fqz1EcyOeIR4Ejx8V5D/rNuLh/2V335wlRJEokVMidSVIAM0c7HQXmuPVqL3Odlhw3nfS3w98GICYxo98wWlP2SD29+IOSQ89ZZ8w5X/Wc85/jnDTFjS0I7yWS2fO9nlzpD5Q953oAePFYziUdG6atHfYezOnX1DcK77yAE3H87Z/8yIHOu9Vj98h0OpGmTOUF8zUdo3akLmoct00BNcGBHBPLTI50a7LYrsqRuTlZ6dicJz7W5iiRG8OJXOmN5fHNoTK6tImE+gRgfniOZLImhzstBZEAeGLN+oAWhEzw1eB2qN1QIH9k3gzhzNE4k8u7Y9keJVowYQ34yrV3vTdS7xSSXPatJhL4Zl0Beh+FVzKVtVGqjP+03tJJH5R9AfNXBhRFEvn44wczdnzFF5/RHnA95iT6FnD0ArP+XTtOAchzVjJ8QvJ9Irdu0HiVq4D0mRldaoAcNl3jVOJ7Dv2lnDMw66XpHjomcCpyerUlS526nFhty1K3ri76A11z1FqZthhQwKCQF0XnMEDcI40OmlAHhfhAUQXGmbz/gwfrQf8fv1XkvR81gA57zbnUhjmtiSzRLtMWObxs8vYoTBLnmnWXmacFUFcQHKiL/XRs8UzVJQAhQH/bcEwwzbEN5kymx4AoHuoIsVI7UZFEeY97uQeedR2YEgBQSk89eZQ6zlPscHaez0k//f1nRN7+ztlAZnmP/a5g0COuOtbVlqbyKLWnUQSEuEdbG0rFjIjXrJWKQlxRbWdgkSvoz2VH+5KBfviObG5aT7nuGUORQU8E93X6x8cDEYb2cM89GRo2AeTEaZEHXyRSpYZHS4MH3XKRYDIU+Y//dra1i5z4JcePW2E/Whc9zvIyTlMIfeA8ZhXCyad7oL7jmiJv3p1MZBtTOB3fWBRoVVmHstGneCgp4AScmsCpn4FlI8Q7vhqzTvuO5Q/X+32du04+qKNefZ+JsNJsNqTZqMsYI8+qFdFibyGX53M1GVdJwXNs+54RMlYsYH/65I3bJnEzg6pn8A9uA/RncHGfqad+29veJr/1W7+lI9hmvX0ugB49LyQlBn4MoCPZUSZHLh8AACAASURBVGkljrlTk6QTMJSBE5x3p3IlEdkEoPfRJGVSRTbqPc8ku7gQG0BvK0Bv+8ztCOiZA/TrGNAlmbxvfaKz0UkSKAy0ayNp0vSabMk02ZVsMpGU6h9FAza5CuyIAXTY1FqtLc3WsvWmI7n0lqYps3knW5KmiVy/crDk4U0veY6aimlvlyfVIf0LgK79b9GbHtLAXHhqbp3qLk5fk88rN9l13C2xD5aeY0MybH1MFujHU6MBbPa3OdwT6pVxUdbYAHr0oCuo1M2zmM2uCYj3nsecZp4zN4DxxNKM5ujbnMo3/PlsjFI5eSgAegGO+RR8LuTzbD5XhiPZGifyyMa2nNvty5X+UEF6GCfBXjJWyHrkqf4C0KvShNGcm1eJO9Vo7Q30/i7rOTbVgu3rewF6sJchfY+Z8AaiTLHgS6Vf9Tjp6CbReaK6wTIHVTfvpoK9M/NdBexLjboej/5kLF//jj86EFAKgD50Bp3XI5mBMdAphIw68+MeVXsSV+4KznFZbxpI58TEgb03ElnfsQIYLBOJV8cBup4DGBfZpD8ZDt14yXvRFxeMyVcRTkVka9v6PHl/gJ/549Z/Djivukw+N2/yMV2fDaAXGZ99vpsds/ZfnL73KQAdJtraFmxMzWGVLKK2aTg4p8fQPC3s/wamQkauoNjbKPAs2JOoOggPH4Pw6NW0y912Dahb9I7YgWO4siqeOF/u9+X6cKiTM1CRWBJlAF1ZE5QiCtBNMWLqGxvxp+/HPSSI47/7qYOBzMPH7lbPD/q/H5qryXKtIscbIp2qyO7QvEmSdKK92oe7dVls13Rm+0q7kxdl8I/YGmdybTCRc9vj/PCyj1DoRNq+0mZ0Yk1WOxhgmnkln5Vkl9foAaKnU1lu49ZelaMO0PmcrOvlHQB6qm1BXPM8F0B9YzDWXlC+57UoAsy36uYQT+FJC8uZZDQdN9pa7BtIzQuFE2WY/uKTBxtR9/KXnpFaw45+DtBV5RXA3OJxnAv573Mc7+M0XU5s17cD9NL8b2Ka7hM+lx7/EWXQAQQALTWlAhBaf22fzzudyonlpiy063IHQL3bUKk7sZh+9O0xPjOoDqwNAKWNFojCtd3PUQN8U5l6TxdfQ91BDvH+Dx1sj/3BbzOJ+8Z2AdCjz3thwWLaISTuncIElcIl4ixk7IBfNRGLXvIwUSUPGIsMYOW9MEngVMZZp7C40/u+yRI5GexFxeiz3sNwlMBqsMTa9859IkKHDI70u64IoFCg78H71il+8nnuPS3yf//xbCCzvMd+548VnydYdMiUUDzme74Hrbx4WgpitAXA+FM04L3b+WXFjFyd4GKdovDk2ei+37ulQ24MurVh+8kuTDlrAjAfWaFZ9zRUM/1iMSsUeedFjt4h8qznYQDsI20CoJcKvAD0P/iZ2dYucuIXHzum8UNZbW+J83qaXaMistxuKWBGXYX5GvGWeKNtcGoIayDdiqlFdh4959FmpdN3HKDHPhFZiY3pLAB6FF/X+gOV0VP44vn2dUpJHcl9vSZjJipRIKMop34b5kOi42QpoHEfkzvb74NE4v0/unYboM+KqZ7Jx98G6M/k6j5Dz/3444/L85//fNklws14yyXur32dtEL+EgyP977Q92wBx4C6zoR2Uzh1UnfJ81Y6lY2EXkSRs0MMZlK5PjS2BRmkjrtq1AXjrXuXugpUnjXfVBMuNcyZimwnFkD6kRzQU4e5zOZErgzpFaRHuiJHm4ks1lNpZAOpT4cq59O7y++SylTG1Az1/dWkUm1IrWa952bFYXLuKaA27SmD/pknDzYH/X95wbM0KOc9rTEXFtDk83vDcTaAXbDnnjo7EDdpNSCOPY3ENG5a3Syx84EA1FBIdw2Cr5mQWbLAHHRn4l0giZlSscH4buFgI+xoY9RayFGDIVQnc2fz4yuPQYr+9f95NlfjcvLwa68JBt22IyM27PjYXHMMBpHyTuVj61vy5E5PHtvalXO7PR86BYtipln0nddVkkbWyzGvSq1uBm7Re56DpGA1c+OaAOqeIOfvJY6QbcnBkkThRAG8vqK5K+vX6VTm6zU53GzKcrMpZxbmjcVTVpOz0c5A5o/+t+84OIN+AeYBYzaXY5LYKIB1gA6bThYW5juw4W3AlH8NqTsJFpc+dhIYzZJAAq65RctsOBYrW++9zNbHagkaPeeqqEDGSQIfJkzuFrx8QqS7ItJoiuBcHGZTvH9k+srae79qVD5ytiq3ozeFwOAm56A/eOSU98wWGVOMSqMASRwK1Qqgm2u7U8PN37wg1Ewx2BQvLPJv882GgmL+hucjTha3wmgwOKa9gN3G5RBDtJVJGHtlkwpUrsykClo7dJROJiM3nywDdBI8ip/aDqM93wbQLdYZaNPEMEnkHY8/cqDC0B3H7hKKmpzDJxtVHXd2olnXiQTj8djBH587lZVOQ+a197uhKoQABMRiqjT0q/eSqmyPh3K1t+NtID4e0c2XMNwDgDMiiPNtNBnrOq332ZOm6pDMsVpu29gwPAC41q/uJipzZ6+giEwsABQPJsg4U+k2qjLfrKp6J2T0jLfDIG6XHvRKTbJ6U5n0tNbQEWQX+9ba9St/cbC2lC95yRkdc6YxxAud1oZVAHTH53mcsVFJOTlYij9FW46ytyXvmFwOHxdQGXEpo54qsx0AvTc0F+j5trm7H5pvSLddkzl+blQVmAPIe7jkY7I3oYXAel4TFBN6XlnxmOIez63qkHhdawDSAsEHP37xQOfdj/4PIp88K7INaKOXm5YH/6oxpS4SQJ3FUNbW1w3wBzOrBcAYs+YxRfeb6EX3WKkMuo8+Ix5aAdbl5w7+AKp7ltefuwzQbV+298HjowOR94b52xJ90w6C+XdGghE7yRm0rz+1wsOpoyK/exMA/dt+1HrQdU8Nabsbgu7po+ffvTjAerG++r4TY/nplSe+s57qQeAAnSKItkHxlbWLTsk4KUPi4eeotiZwjFBusVewbyXur1ARGezYWgzpP98VqSciNd6Xv+fOqsj8CZH5VZHDp60KXWbttXjghROY+L/8pYMB9BcdO2rTbErMebQlkQ/xb3cuLPpcdAPoGFNaW5FdF3p4o1UrfvZ8uaxW5XGhoDJFlRW/NOuI9rtSkYDfY2Iae0a0r9gxdmNfV0pQAh2KKztLRVryyTEMfMKoxqEZ2o6R4XvbSpbJh65dm+l6nRF+3H74jCtwG6DPuGBf6IcPBgP5gR/4AXnHO94hn/zkJ2d+O2WATn9KmUcMwInxlhpiqWGYMenMlSUJVCd1Z3LWk0yujzO5MpkqQMfMi17JlkxlpZpp4qSsVKMu9y11ZRGTuLmG9u3opo78cYKr7lTd3nHVpR+ZWawf2ZrINXquHd4cayayXM+kOWXk2tiDfYyAYaBPJuOKDt/Sv6HMWqlCFxrwi8Cpwp+0L0mayKOPz8YEx9r9yBfdp+sQlcec5Xaju2DPDXAam6FFjdidC6WjAnTuIXH3/bswalPneZfGurMQwJtbYoNncokk70kN1ADqmgTmdf/SeWIIP/8figOVeNt7MCY+TKfMPE2Zfu/Fog/2v3v3wQH6rz4NQI+GXB0F40UbpgQ8srEl53f78pmdns5DVyk7rLWPoAOg4zFgQBqAbrLzpwD0XLJmfdPKdvuKRJHAcgvrxYpjwLmkzLq7uAZIC6ZazdIArSJ6jh9rt3SG9un5bu4kb7UU3vktAOhbBqjVAAlPBdgGJO7OpANmQ0bIVwXmNVpM7K6MkidUAG0q/Iwd4k5vpCUHnvyr07n1fcZ68JrRB0qCzI3kWaXvAbo5hhUbg7OwYuCcx0Y/K+C8/7kAesgU9M3cGoD+7MMnfURTkSKFd0Q4o/vLaczScX2aeJmnQxx3A9K2PgrQSdCcrVWA7q79vIqeY/skiDoa0X0QeEQkWSR52sfIDN4ciMNgptp3riMWfSwjkkSbWmBgHCAcIxNtpi6svxn+5AA9S+WPDhjr7rvjHmlMAehTOVKvSIf5uo2aKqAmJHc421P0nKay1GaMV02LG+X2gGq9KbU6c89xw6/L1mggl3a31f2/q20ZYbRIQclA45G5lv4eU0+Kw4ygY2xkrBVu7gCF5XZTi2FrfYzoMCY1ZRc+HcRfgDa+GXMAdO6tmqx0bE4hx4LWmEFaVWCe1Zpa4FWA/tetPJcYJze+CYD+4tPSCL8NH4uZX18B2MvnSN5OtL8fvWCJQ8m0F8Tb/hZTPHSWemx4ym4bAlTH5myqagRk74BxvDyW5hhPV5N5etObVQWMqjBwBr03yRSgs/6jCWDcZeQB0L3f3DY5i6Cc+zD5H3n0YAD9x75T5NPnRXYwDHODt/ha9TGTc3NWKMzjtQMjAGVZ8aMyci8GausHyxGssEvceR7ePgVEli+XcTvgCvf3QIYA27wbLC54Vx3xGmoM567t/IwUf3WxiK2AcUCpGe4VxnWonI4fFvm/bkLi/i0/omIQvUXDmu6tIXG3+r7eKKwSu/sDWzNlzBOR7R0RpOgK0Ecl9ZR7AOieaHY/TwHo0zJAj6KuYVBvhBepZNaOxfm8y77WExltG0Bv0NLoAD2l7eqwyOJpmwSyeNyCaw7Q/fgEQIdBf/+MDviR1wHQy21JehxdcYecnULs3UtLOrJssdlS8skM2uz606vWr+EA6b6rPsUwNK4R/0M952LsWuwTxD9nMFzdYXtBAPIo7uVu75A6gHOYfJ94A4Nu+akVfokBmNhuDWxqyY3BQAkynbqQZfKBq7cBuh2zvx232wD9b8dxeNp3sbKyssckjotoZ2dH5ubm5K1vfat8zdd8zczvvgDor9VxXRHE+cqGYlVx64U0BjZGbpksm5hBhR0AdWmYyJP9iVwdZXJ2YHJ4EshuzfsUazVZajXVVRf39k6tIiuMd1Epmcl0AOgkQxvu/r42TtWI7kn64MgSvCx9tDmVpXomrcpUGXoSQOSdBsgY6DOVCfdpRYZmiSGZAlVAlLnfTpWRZgxcIpN0In/xsffOVC2MtfufnnePBmU2iXBrjyCpPWz++3I3t4F0lyL69qL9ps5+W0Kfh/k9jtORyMemGm7RjBuLv7NRIMbW6DzfiOy5iUuspScJIaUPYF4aHRXFAk2cvTe+7cCXqus3v3c2No5PFWv3K69+WPtDucUM05AAb03oQU/lYn8oG+NETeMwjNucmC9BUqnq3TZCu5NQBxdvxQVP+BlVJ957XwLofB6VKgcD5cmsWU75rF2/qkjy4/Ob47tL5KeZGhx2MElrMvu5LUvNhhzvdLSQAavph1KvHxh2boObZdAdoOscdJcsmprF0wAf0RSj5OokQVzT4KfEAA3AnLvODfaiEcnZjY2idxNZ/IL3e6oknvcOs+Sgnb9DAqnyTO+j1BqSu0Hz7YkTIiurXpSCafc7kll8JTTJjwvEE7eQXpazr1vBoN936LheGwZcnzrszNbQuRJfyzB1jKJljCaMa9iAOSx7Vbr1hrWcIBd01tquZO/n96tR5936P3CNIZNs1+qy0Gwp8EQaCSDXyQFaGA2DTDMupM2Ic0iBeaOuf4tRHY81E0Wb8EASxwieMCKaZIn85YXZJi/kTuTPuV9ONKs6kaDrbuJjRnGRQE8SjanMOifqqUu4x2PWa1qtCCx1tdYUBem1hjTqbW312Bj1JE0SSYdDj9HELnxEUmXQjy+0tJDL52bNUROYuaZZOmMuR8J5dA62vqoGppxb20lF+qk5tCNhp/OS9heYbO4YzM01HQArOOZ1cY4HpNe1kMP+wZ50A+f8JJE//sh7DrRPvOxFp6WBZDaqOrl6qTT2sKTSsTy/pNoJNs3DXciqLW93eXypXz0Sdi9H50VIdX3Xjd570THLC7l6qEfUb8LMN2vc3aQU13d1yU8y2Rmmst5PZJxMpT8iLwCE2z7IsbCXCG24+bI89viVA63dv/lukXOXRHb6xqKryZoDzpChR6E1WGBikgJMGHKY4CgkuCmZXSCFz4XmBuxDmFkS59RjxeJigMfw+GNUm0+0yuXe++xaTELsY9d4TgWwzjITb2HRKagibec98x4x3WzCdns8ZyLGoSWR33zHbCxweY/9xjeKNDp2yAMTu7gql14ooGWM4RUzaKNAS18466yyfCT58T6xmCjdHI/qc+up7dXueK0A6BH/wkhPWxAwe4MRb1vxlfvVyyJb6yZrTwY+upO9hUkt7EXHRVbuFqkwKWSucNDXjiEvvMQeSOHlkbfNtna5xP24M+h7P67mvfcuLyswv0cBuk3tsMu1VEyLgkguBSs8S8pPmedzrhiMPYaYzRqG4WfI7POikDfahfrLpsewr9m1p/+F4JjYxBF8ozCsiz2KAgOtd8xhvz7oy+ZwJB9fv6H7Fjfavd5z+TZA33f4v6A/3gboX9Dl/9wvjhlc+QYYPXLkiHzxF3+xAN4PcsvHrL3WALpurlGFjDnY3tOGPNfFOzZCyDMAesR300wuDhJ5sjeRa+NUnhzQow6wz5QVwUiIQLPUaqir7qluS0HNfM06erV3UY3BCoBOUoRzOwWAy+NUHXojAB5piiwBLqjI6vgqehYbaqxB1dMMXwDp5s5rQ8YMoLegFhSgU0RA/kMSnMiff/SvDpQ8/MBz79HE0caWBOjeazKzX+IeACAAQoCCSBpsM4sUy3u+HLHkXiM5MeJboRdYtcdrz4xzr+bqDlZs0vHssbkYOxisexhPFYwhhXCMlShGGEA3A5Nvf/9syoNy8vCWVz+s5k3c8vfhGxVGWLCG53sD2RhP5AK956OJ7DIVgA0b1i7AubcumErBksQyQGcUUwD0YDN5/zZubS9Aj0SPr3otFIfBQILLmrUNwQH3XI1zuSorzZYcmzN/Bcapqat73pNsB+yZAOi8RBa9fl5t0HMIEO2fr5qKjlUhaeEeAJ0kCem7nm5VkX5fZO2GJYwAbh63MmfJFJJLPgWGcsFScFrC5MfooLIru7o9T0VOAtAJUf7e6Isj2UI6i6+EMuqfJ4B+7+pxky6qCdReh9wAO+Xrk2sU5gEQQ9GRr/nj/Ho3czgD6PQk2rxxKwCW5YdEu3xigEsZVdZeETne7WqxarHV1mLj9gRAmNp8WgVQVsQ0oFpVx/ndyViZHBgd2PPlVktwpGeMmcUUUziZZ4hJH2Gg33PpYAD97z/vATndqkkLAKdgfCrbA1iYTLIJ0mmrABFtKTJEbzyfGV8RADoS92qtIfV6U5qNjhYPdidDmUzGMuz39TkphHGmYfnItXuUVijvT+e4acFB19a8J7Zdpn0UaXa9mo9M25hUZCexOeqoslBTwf8r6GTUJOc0LvA5W416AZjG6EobswZAR9W1OUl0jONfPfr+A+0TL30hDHoUFPNas8KaAmxHsaAo6Oi/lwo8IXuPcYp5UTfUQPuUGlrI9b3b4k8gNUvlYc8B1ADv4tzmureJKqy/nt8NK2gomE+nOkXkRj9RFn13hEs7zxU+KVbkMp8Fex0A+uNnrx5o7X4SgH7ZGHSM4kaAOVzSdQ/yWBQSdp85rn3TDqQDnAM2yxLoAslYaGo6Iwwbz7omHvuVBFb/FjsutANF/OPDqeS93KLjyRijAEOlpIVUB+icd8xq15ne4eeBlLsm0jY/Rd17YPLprf+N/zgbyCzvsW/4VyJNAHq4rLv/jpZO/PNpf3kqcuGiyI0bZmIHQCd+l83hzMOgYP4jX1EsGIVhf85cfVbq++dxfEbOXXxQAOlnjoosdm0d2G8A6JvrIimmpyMD6BSWAejcu0dFls+IVDCLc1NSVXA5QA+2WvfvicgnfnO2tYuc+KUnjEGPW+QNxLQHVlblcKcjdy4uykq7nTPZ9tpFuxw5LXHbanJe6HJyI8xCy+tWqC8pgj0VoNsmbbf9OaSy97xfz/GiMIdnFOAbcL42HGhBlyIy+wVFYYrA1zHiHQ3lY2s3dMyo7o1ZJn95abbr9SAY5Pbf/M1X4DZA/5uv1f8vHhnB6Dde91ppaWJpyXIu9YwEP5J8jw98Yb45icu1EfNSE1lD3k5VfZyo463xFamy3AvVqQYP6+kUma+74Y2bqo3pb4f5UcMLWDXkjxXZYT5uxsxyM9nAfIhS76FWTRbqJl9WhpdksEa/sYFT24Ttb/hbmFUAujkc206FVJwEgnFrSNw//qmDJV7f+9DdyjAEW2YumMUmW/xsvT0x8sYSzCLQ7ufycmWiZxEGzO1vPEI/5RxUxjwAesz2zqu6e3h0730M2XbJjM5BhLHw9jcq43YGGUa+5esOwPm+Dz02U9JVTh5+/iteoWAkmEjNGXwT6iWpMKbu6nAs25NEbowT2cF0BV+CDMNA5FtmChfKhfJX3nhIQEnu+STBWhb9vzmJYOy25ywGwn1UnwNyGD1mD1uveaZAGyC+VAeMt+R4p6195kuNphaKOM1C4rb3QNlFBMD6ut8/uEnchdIc9Bjzo08cAJ16lks2Q4KotQJ+x4g0Tx71OMP0uGoDifzODXc33jVjuGPL5pjMY0l2N3eNXemTVGEc5c7Dkc4Ei6XmSCJy9JD1XDa7xuaM6XlvFGObImmO5MLz+qKa5MnkrWDQ71k5pkobbsQcXtuYXmKDte1E3x/KHhIVkhgSFwPphnDiMuSbvN0Bgzbv+w5gXrArznR6Vqwywmkma8O+Mo0PrK6Y2VCzrdL0jeFIe85Dxmiz2oumi63RUNYHQ1lsNeVwuyNL7Zac6HZVdbIxHDpjbnEpxiRG4vWeiwcD6K9/8XPlRKsqTU6sZKJ9xZe3BtIbJTIYmfycwiefjfeFeuTQXEuW2k0tmFI8UNtKbUHBVbAlVfwjYHJGQ9nc2VY6sZomqtjC2FK9S1pW/FjtNIxFr1jyikQe09DruxQzMlloTKVZnepzE/PPDyuyNhZtvUKVhfHZyBuUbcKHnfsGiGwAmc4J968hSeLfbSxnKuee/PhM8S6XzD7/VC5xJz4Y4+YxIsB1Weq+p/e8aK8pA3SLl/afMPLch4fsei8FICvcxD7PtevFJ2KpM8ZWfzegQRzjbu0xtHuIjojkCHBM++OpbPRTgVXeHVibGaA5b+VyMKFrd2E2Ri7WDoD+xHmRzW2Rxy+I9EciXYwpS5J2zN7U9sELfVpgdGl5+HBEa3wYVIbvhe5xFJ2aJtGemzd38IaP7wI4cmMddJoFZpcY0EXPNmDWW3W4RKMo2moYCOUOGw67HMdJfXcoapiPrgFf7+Xm/aEA4f0cOyLylt+ZDWSW99g3vNEB+l5VuS4Tx4m1uXpdZHvXRpxtbrmTPAUDVwCEyR3pF6CX/YO7ZiSl87S8z+V5iv8yciLqX6xDt2sFiFNHbN47zD1u+oxXo0isz++vw/ccWu7sH815M75rzdvPc8u2drnbvr9mMhJ59LdmW7s45155+oTuCxHnTeXU1gLsPUvLCswXcHGv1dU4Wf1BkIxrv4NlczpqDYmE32jxJMZHSxSMtxp5uhGdZXh27QDuIx/yS9yvY7ua1S+lpAILg9AgfcK4ljyD3vKLvZ48trmpRWlGHvJejs91zJA3TbQYfG3Q17yZGMOe8a7zs0022pvn3P7pVq/AbYB+q1f0Fj/f5uamvOUtb5FHH31Un/mhhx6S17/+9bK0xHDh2W/7AbrKsd2ZNSSmOrZEExa37/KA3HNp++VhIleGibrfXh9lOgbo2hBu0wA6jr/MzQU4Wg+zsdi5zFwTt4kGHAAZG/x4Wpd0WpV+VleZunVXA6rNIfhIu6k97Mr4smPG3ZeAnnYdKePza1X6rA7uFZnD8dsr+gTCsUvxH/vMwdx5vxuArj2OJQdmT+DLQL1cHQ0jjmAZyj1KBcFRSJVyye3TAXRlSDwJy/ujDVxHG4KJN3NFXx74rafawbl/b2Ob7LFPD9DtOJIIAhq+/8OzmeuVk4efe9UrdIMqz+kNgN5H3qsqCljzTJC842vA9xRyaKvA6d36mYs5vRwH29ysqqSFBTfeCoBeqAbshNFtGEmiF6JQCuj3Zdd6E74qSKdNgs/PyLSVZkNl7SfnOlqAojptAK44fnuvzADoiXzd7//hgZJ9XNw/G0BnTqzevAc9Lg3t/7bRqqYaKEllwxSQzwxA7wHQRyK9nQKgK5BxphuADjuFAzFJqjJTOinAnlt70D354/eHV0QW50XajESCLWnY3Ypazn4EOPAsRRM8vweYeUYAuitDONYASnP6twsgZOUkLzZu0kBygJzofjRAZwwJbu/2EQzwmYLCAZmea0VxgOe60tvRpOnBQwD0tswjcVeAbvNzo8ew3G+IMgnGg55B2ipgc0gY71iY10QLgG6FVitWWlHBjj2J13suPXGg8+6fvvghOdasCm4l02QiaZrKFQfoozHmoWnuHk47E4qAYwsdOTxnzD793xaJajJlbF2tKXVG2DVbMhiN5Mb2lgP0sRoqAgi1gIQbe60ih+Zs1nAV1yhM49KGTNKKXAWgTzKZwzi0OlWGHt+Rs4OpXBuLXBtlcgOAjimVhgZ1aovOmD1KB91T/HhqkquVNhtTBMi8emE2xVDssS98HgDdorB5TxU9qkGMabyJ69LPQcfweSyPInRI5e16LpRAebtOQItgNv1yUkM3PQ+sEI9E3RzZo3gcbKjFRI41cQHVhConaAuo0bJg4Gw4mcrWEIA+le2+sesDN1EzDOd9slkm5y8eDKD/1HeLPH7OXNwxi+sNRBYWDaDHeuH8rcZlDtDzOefePlNmzvWa8DGRel1QhKgZq8tzAv45hRrzdtGAsayQZ3FzvmuxlCIlALeH7N5VRIBEnc1OUaNpwDxAesTeoMi1kElxk2NRmsXOzwrQWyLHj4j80v85G8gs77Fv+NdFD3qEVGXGKTphCpqIXLxixY+1Nes3DwO3mCVvuYu3R+0H6HE57Us/YwSdRjuln+0BOMjzfZeCbbOQuPPazD0HpNMLr+ov/sCBOu+XJdK6HoUUJpF0DaQvHC38UvK3x8GkhQAAIABJREFUQR45Evn4ARn0V915Uq+5uAGmUTkxWu3M4qIstVCBYlJZlc2RGUiiVLLWJjP8BBhvjpg4YzeMRI90yBOsHUl9mVB++iQOvVpccRJqzHj9UPlEua0A6FZkNr8U94SaZnn+x+SJ3mQs53d35RMbG/r+YOchE44hFXElGb+n0GCtlgbQ/+TcbYC+77T+gv54G6B/QZf/c7/4+973Pvmqr/oq6XQ68rKXvUwf/N73vlcwivvDP/xDedGLXjTzu88B+mtfJw2cdnVUmO3ouazLN/+oClqSOpUbo0R7xs/1x3KuN5adBIm6SXMIVgBp7sZM+kihoMa81MmsaDOkGJn8kp81oLcUUE/V2A1Aze+p/I3UhOi+pSU50Z2TbrMl7UZLro3G6hjPWAsqmcri6LgfS04r1ZpUPBmcY2gqAYjsYppJJR1LliVy7smDjR76tgfu9EqrgURjY+wWwDs4t1Am5I7veQW0zDiEmZwl+fuZ86JnyV/MXysAVzhQF2PaDB5Eorc34EeS6IlwaVyYMehWnLFReiZxt2TNih28l+98/6dmSvZ5/Tjvfu5VDxtA93mg+nl93TgP+R6zQDMpwvSJam+q4JzjzPEeZvYz/8ZjeKzO6kXloaYuZhaneUEYe/nGG5te+NjQLsG6IeFXt3qKUvwhYwKnmXSQelYrcqjZkOVmQ1Zxam+ZYQxSfRvHZsny3lv5F7cGoJ8vmcTFsdfNlqQvNRYcsx2SQ2WFlkTa827UhjwwzptgyQCqFLR6IptrxgghO+S5kcBzLmsSTPKIk6/3JmriCvhHtsgInKqxMJgJIQUlGVw9LDK/KLJwSGR+yaSKaS1aN4o+0GDQcwDs45sisYW9GWxZ8eCRd8+WtMY598Dhk+ZXkNP1NsImkiQttrlUMRhzHNVDsh4xJdh3rgGuOYA5ACsAOsvLcwajHuNyGE8Zn4fnuNrfVePNw3NtPYdW2nPKynBu66xcd5bPPSu0QMDIsZFshGSx0dCE8eT8vCZWAHvinnYFuSJGwypmXbjzXjk30zWb92Xec7cscqyJ6YVJiY7qWazWtDd9ODYmfbHF/PGqnFlqy4n5pvqMUIioYNhZqesos6yi8E/l5IPxRDZ6falOU2lNJ6q0Qq5OTF13yX6nYSqoTotrrSq9tCnjrCprfZv00KylCuzH9JpLRc72MwXn26mozN0YpxiJ6Ncka+IzgvfHCD1+alPN39LnnsqVcwdj0F/4XGfQVbGyN0ZEG01Z6m7F00JRFG0VRWFtL8APYF6Icn0P8vM8rqlwCcfkTfd7HcP21JnYoVnWUZQaR80/g1jJHsB13qhX1GsAs70JDu8jRuBlcmV7ov3bXP/xLlCsXb5y/UDn3U9+l8jZCwYiP3XOpO4w5Lz/kPoH+IzApgC95Nyue3HcHbSj/NFYjSoAObnPVF9dNYYcGb0WMxw8B6M8xzi3qvVqU4zcwXnce7RJbRa61sfenbOv4eKuQJxWI5fL58lCFCJ9z9XXnIq0AOiHRX72388W68p77D99Y+FKrxL1zHr5UTysrRtbrey1jzdjEojK9T2JiZ2Lr+pfMrY2Ce76b0894fKtj7XlM2BvdPiwCIZ+1zbttVsdMwzFu4VzanPDxt1p0YJCS5mZV6NHe9ooLvMzLQ5zKyJH77bXmV8oxulxrJHp/+cDmsS9+k68SuxFiesUTe9ZWtE4u9wGZDfyfcIKuJnmvgBd8l+Y6/XhUPu7da9l7Koy6B1Vai02mrLQbMqdiwuaA4VXjamdQqlV7NNR5I3cwqb6THP/psgt2atQfNlLWj6uBnDDoVzu7+r7w7uEwgIg3fa9YOWLnJJ4/c5zl2e6XvdnPbd/vrUrcBug39r1vKXP9vDDD8t9990nb37zm5V10GQ8SeQbvuEbhFFrf/qnfzrz6+Vz0L/6tdJA4u5JHWEh2ESTA3sVnH5MlX2atH1jnMrZ3lhBOm7ru4nNUhwljGsguQy5jwUo7V0HeDkw168A74xhECYhtIDYVifuaq0jFaL6FEEdAH2gY34eWF2VU/PzstiZk/lWW873RnKhP9LeS+YETzHYSHlOe02eA+fgOr037K5qJAcFkEktGepznj83mxt5rN03K0B3WX0w/RaR85Fh+/uFwrCpkAFakmTgnucq3N7DcM7+3dewdKRz53GXOe4F6KXxHSUWLza6ctAvHKqdfQ9Jn5hrO0Ks6MHGuInzgvf7He/95MxBPNbuZ1/1sAKY2JDydSkK5/loPNh0QDsSXpg4qtUK0lOc/i1B56upJqxvFJUH5m4mWXb56lOS3oI5R7qvzIdOKDBDQTWMo9DE+LQaI7cqcrzdkkMtG6MGg6lO8sjhov8rFA17xKXlVAeJ+80x6AB0zNrCJM73WHNST0Q2Lov0t42l4DHzKyKdJWMeuEfFJtzn6fNrMGKtL7IFQPeZtyrhVvWKMRs6Es1ZH2Ws8HWA0XBzJZLYNRI++hiRmQLQjxowXzok0l22fnmYlDxhtsslX7/930cemDPo44MD9IeO3lFiwG0dimKWgTc13gnmT0emWQIWIFkNMFVpZHFRZ4/XzOXdxuwUwJhkjBtxMz6vfh53Bb7W76kskpFfyLgB6DAs4cuhzHmpmGDxgNnUmP8M8ykLOAlTtATQ8n61dx6JqIIBk21zrADoH712YaZrNq7X+86ckTlk4QrWaPMQacPeSEWONGzcGlJ39oDlVlXd0u9apnCAeZ2P36oyUQMjPQzbagqYJ1lVhpNENvtMHc+kWyGBrKihKOtwqT+2/ns126pKt91QgL4TAH1IIgpAz6RaxfjTCnvnBqlcHzEVhLYpu/44BsQyM/a0C1XHzzlzZKojjxe0RDWNaVeVT5bK5QPuEzlA1z7cfQDdTpi8cGOFvoId1rMp/3crppowwwGEn09WhI3Sm20SeZHYYxO95urRovLsmHxi49F828q/5hdKnLjWk6WvoUWpekW6TRvNpmaE40x2R6lc3JxoT3sw6bwPAPrVqwcH6E9eNID+mQsG0AHHaqzmigMFcB5idR+NPvFy8WEfQIc95m8wMoM9P7xkRcbDhwygo2znucIQLgC6StwrIjuuJIL5RVGk6ctUZGnBZpjDtPN8xGTth/dxa9qXHuPxwszTsS7Pq++ffaglcuywyL+b0eiM9Y5r9ut+yFQB+jm8aIESYTASuXrN2H9k5RRdlVanqBEAPaoefu4pQGemPNdQvG9vEXFeJ89MrNhlPfXsQSdPWiH3/DXbt5oUc2mbsm5I2WFs6KA4Z8sAXQ9rAHRfJ1VaUgxZFTlxv73O8oq9JiGXtw7g/5M3z1bciHV79Z3H8hyHWAFAv3dlVZZbbZlrmJEne4CqUHwWOeCXQu7ueKwmnyicrvZ7+hhijPmEtBWgLzRaWty/b3lZWnWbAsKN+JxPBIo2yD35XoBp8yRh71CPEnVmt7yIHClUibi08/POeKQgnccPk4lP47E8LqbzFDovy/P/6Nxspo4zA5DbfzDTCtwG6DMt1+f3wTDnH/zgB+WBBx7Y88KPPPKIvOQlL5E+JdAZbxGM/tcv/wqZ8xklUYkLMGjSTqsOctHiBkkCSE/w1sRGq9F/DoBCQggbjRSd56HKBxiuwpoQY/MebAsgAGPkmlk21KSzGGyFRBEzn7bK2LX3HDO3ZCDZdKyVzOPdeTm5sCxHugtyqZ/I5QEVy7Fc749kimQ+Nf92wp06Bjc60mTMW3dOkzvtg5xm0oBBTxP52GcO1oP+Tc8+Y+DMe881GXHzqKicWsr01B70kL0Hu2H7oQP0Uj+SPqdnT4XpVGFGUkiWLfGMpDzvdyv2N9vnciOhSPbMvIrPoaPCAJwk9sq+maRdx025QRVMH4+Hqf6mP5+NUSonDz/zd16u7te2/xZVXGMQrBihTHoULyKDdOUBa4GZIEw5LLrK35NU1un9oqdqhJmUV6HDBE8TZIbPFckdI56AMEg3NXmPueZTyjiZrDbr0q3V5Gi7KcucQ3WqzzVzdddFxlTKM0Vf3zyjyEH6XoCO9OxmJO4B0Fk+TdidyQCUw3xfOyuys27JCskgckDMggDIc4siHVgeGAcYcNiuvkgP91yMi2CDMnfx9bFrnKuYHMF6IAEN5oqXNdBWGAqt0UfIeCM3aTp12hiUOm7wLZGEsW4xL9cuDTu/o4C/D6zzEC0S+Bx0+jY/fkAG/QXHT7uRoLfsuJxZhRJ+3VopLI5gRU3JNPnynvRIuOK8NWl79C673FiLPUVPusZCZzU0EXMZJGwL36sLr8rs6bNWkbVex1Ggi7Mn1De8H0zLDNjhuI1BHVesPVITR3q+FczZyaF/m2XyxMbBpMZnTt2pRTpNvr38OD+1SRpHeH2tAKTKqB/qMGe8KqcWW3KsS0+4cq+SVuuSVBsqSe+PMxmOE9kdjBTUD5KJMPXseAem3AzK6C0/uzPUohxj0vi09WZbe8xvJHUZZFW5MTLDQfqjAT+0vhCbdpgSQAHAVTU6ckwBuhlEajiBQddihpmQ6lo5SAJIMyrOPAa4JlI5f/ZgSqsXPRcXd3vumIPugS+H1PuBSM6gl8F7KVbqsY1aW5wgnyUHyAvA6t7urWzesmI92QWTrvHWL8gyNo/WLFWl0asKy1sTWWgaWMcRn0OkI9kmU1nrWZuSjmNLM1m7tnagwtBPfJfI+UvWUsMd5nsdQOdjwQJ4ev3Art0A7h5TYga6Akn/3NqegyLIgT7nD4ojOgb5+xyY+6zvIIt5PV6LgiWPoXBEPKQQSrxVWbt/zyGPKRXqiB4j3ih81uxvVJ3GqeH+IFaoN3B/8pjIL95ED/p/+S+LXn2/POXiVSsu3FgzEDsdGTte4V7q+87X0M8pTcNYK2fQ41TL1Qv5Xmt7rq5J217/2DHbO66sG4PO/gNAT3bNDI79R/vxS2oCjfteII78Kuow2kM/FVk+KnL3Q763Ldn1z/MSblE2vPN/PxhAf8XJFZe4U6RHhVeR5XZXvZoa6ndkxAL/Vz8LqahZJ6y0Fb6myqBf6/dV6g44JmeikAu4R9oeDDq/43vzD7JVjTjP/gAAp/1TDUU1Zpmho3oo+b5kEz8gKqwdK0geq9DZ35qHCaSFfaZo+SuAeZGJqcT94mwFtRnhx+2Hz7gCtwH6jAv2+Xz4sWPH5Nd//dflK7/yK/e87B/8wR/I13/918vVq1dnfjsB0H/s5a+ULhohn8erUNmrg1TkcBJmtA3VNyqD/I5xV7iubyZT2YIpUzMiNnlY80k+usWY8JYldQ66dPyKJosAdOaxAqYLgD7VgSdVqfN37GJaus1knPYlzSZy5+KyHJ3rypmlVTk+vyRXB6lcHSSyNhjL1f5Ismy8D6DT5ziXA3Q1AkKSOs2kmWJ2NJEPPPa+AyUP3/jsMyqdtn7yCK7ePutlZVUOlPuLFHTa3cB5fLVednusP4dvArZvlV2hA6CbDDvvF/c+f5XtlpgXC70m+ba81BPtmO2sxmZVTcADoFPT1U1FATubi/U50ccPmIelfv2fzaY84LXjvHvT33l5Lu2Kfq9oE1AJl1eeo4eWNVADt5JTMe8BxhyAzvm4kyTa8rA9SXV2cfgXaO+n/y0bZCTdrB0AnaR9DmWAN1WQzNem+D5ncrzTkqVGXU502tp3PofhoY98sg2vqg7VtruWDAFyPiv4y7hEK3LLAbr3l+9uiIz6IlceF9m+4XJU5OskSi1jHJhJ3l0SWVw1h1xaegfMur3mM26dMSeZRMK5tW0fK5Kt5UMmm48iR9cZFdx/SZ5vANBHDtBTkbvvEjl21OcYAc4bBUA3dvLzB9BfdOLM0wB0uzqCvYijFNcPEsYwilOpu1+7AaC0HSIH6M6e+zWDfJGbnsNeYOJ6B+wbiDa2O85pHg+wjxFlwdqXEzeeSl3dnW3hZ5PNmsEd10gAdE1yHYza9ZTKhc0bB4p1d9xxl8mzS22lC4xUm07lCJJ03kOW6TV0pFOVxVZFTiy05CiDyisNZc4nlbqMqw3pjxLZHkxkOJrI9m5fGSDMQucbFTm1gBeAXesoZB7fGsogyYQZ3DRNVeptSSs1uTapKTO+Pqk4QKfIaAam+nxS00kP2o/vQZWiLEU4G+to12wAdOvlNhDN40NObwCqoq1TZ584GEB/8XNPS71uRZc8C89BdRSEInbYfhEx2t9mMU5NP4vLfaN9KWeP/W/zUFMoB/TPHIhbAu8u7vuAegB0O/dsT9JrVHvXvZjhBhIcp05tKrQfrHTt/KM/HYB+dReH/6kMxhixZrJ29WAA/ce/U+TCZes91/7pzEZBDnzGuQJt7wePy0zZbm+jifii/l3xWVACOWDujaIabnFtYd7W1utfCtrz8E4BwvvdYeBJZRDlQYDCeAO4AexqqunXSYBL3rv1+tvfERrgRXivFDr0mEb/fsUA+h3HRd789tlAZnmPffW3PBWgU+zAFG6DNqi+SA1gjvQe8I0qinhi3Ire89ClJ4AVLmHQo6Bqha5QAHr+YnVr7RcHoB8+YgD9+qbtERjxsUYjTOlomcLN3tdTCyxlZYEXbHn5vFDpPekrR0Xufb5Iq20eJ3sA+rbIH/7qbGsXucmXHFvS5/KPpuc17ZS1al2qtEzGPDn2Vlr+KlXtL19tQSpZ7rU+GsnaYKBM+iVkFnrNmsqKQiysOb4htMjRk05s1TnqrtghLjEijUIrLU9m4OYjYH1KQgD0kNfHz6FYVYNfN0E0pZiP/POcMdRqHlLcPNj2l3ddmm2fiJBz++szswK3Afozs6635Fm/4zu+Q97+9rfLT//0T8uXfumX6nO++93vlu/5nu+Rr/3ar5U3velNM79OBKPXP/f5WhUkMMZMcpXvaF8vsmELEHzVnzH8YTavmqxVZTSFZwxTIsbt0INm81Kr1bo0GnM2F7Q0ckWZc6Tr01QSorNW98yACfkjYajOzFzte7akcJj0JMkmKhPqNkj8FmV1DmMkWK6K7DIje8z7pKgwNuM2reTWpNloaQJbMCImI0RsSaHgY48fLPF6/f2n9zDothmXLMIsX3JnzzDisepnIT8sAfScLfbfldn4EsPn2X7uChuGcGbwZoZVwbAUm4wxxbkxnDJDwZ4bQFfmyhkjyiTBnMMWw5wrU9eo6/MDjP/Juz4yU7JfTh5+8SseLjHotkwmLy4KHJEcmqC4YNpDzImcdZBN1eH9BjLZ8UQuDoZqKIfKQyv5MQ/de8vZEPmdGk4pbrTKtE5Apm+OfwNktBsyX4c5b8lCAxYdYO4gPgbR8151Bnto8Cwti58LntYLI55i3CqJezDojiuUPU/HIpdh0G9wQdtFoP2PyJFJItsiC8siS6vmTExySM/5iNE2LmEnOSUBVqsGDJMYbXjE/r6z6Iy9O8U3YGAmIuvXLOHb7ZmkM9zd7zhm44IaXWPRJ027B0YK1iSS5z1JA4miYxokosNNM2Q6aA/6i0/c6ePVHOGU2igj9oWreySDxDviIWAaloLHcY9/j8JP+bqyubSYUiIA9/M62ny82MbnVnf4KNaVHOVDah2gPhIou15t3q3FDy/0+ZhHTZ5DH+rpZchmtac+S+WJjdmYkdgnTp+8MweX9lmnspCl0pJMlrJE2pLJYqUq7UpFFlTiXpFD8y1Z7jQUVFOoHVXqgpB9ZzCStZ2+VJJEamM3CZ0mKos/OY/cn3iDDF7k+gCZ5lTWhphDilwZM99cZC2tyXBakZHUdc+w6xlAabGCPnQr0IV8mzYDuyKtuBStKabSsWUzfwKar1Si20DZAAit6hi5Tz4xm2IoN4l76JQy6HbO76W7ywxkHOcCGfj5n7+/AixqYUiN9Czes8mSwHOjCKGxTQsq5qvgG5HJnX1/x4WdfnQAtRrHBYCPvcbfKmqCeq1qhXdVvxnYV+NM1wBEuxVso0mNbeRpb2jS308/MdvYpli7//lfiDzxpLHn6sjOdVMG5A6adW63y8gBzADAMKuMlp3ow1bndFRCtPW40TaAnjgKcFYlAHFNDbjcYwNrHMZMUihAaeTO7YBQ/haADphnxnkH53Yc3H36hXtF6hPkxp0wzID/AJ5cw94+xOdTk7ijIj//H2YDmeU99sv+uX0eeBNElsR5WgR47z3UVrQtwaKzBhjAxcg4Nt7CcbA4Hb1tRgtc5vdo7vTua4I0XxUaflJrr7oaPdpX7TRBvj9n72u0KZIAznkfJRd+y5kKBt3a3/IapxWLRASAftdDXgigexFVgpvK4FXyx78x29rFOfcyAHp+nXq7ooLyGOXqrVAoiGpmLnrv0rKcWlhUwI2PCC14MOAXdnfkk+sb+j2mcTHRCGn7MqagMOqNhj4HQJ3r2dqoMpOle187+4S1dHgLZKnQG3tIXOXkbZHbkceFytL4g8hKIiR4Ea+EINjv3nt9c+bcbmYQcvsP/sYrcBug/42X6vP/wPF4rGD8F37hF7T3nFuj0ZBv/uZvlp/4iZ+QljLgs90iGP29++6XWhWuEMkMvS2Zbtpc9PEzX5kHrMyNJzxIZdTsR7dopNkkAlT6ogctVUfdZqNrCZE6NZvM0lh6jNx4raF+tdTKgruypQ7QqVCSZAzTviTpWGVGzWpdVjvzstSe8xShphLIQWJmc+NposkZs0yRI9FjrzO8GfGm8m1+jyO3SRcfPXswd95/pgA95qB7FTl6kZ05V2llKSkvB9M8OfMNSDeipzOPKz1HJFp8jV7FcF4PR9/yKDFjjy0RKIC8jZYigSPhNxbQKrjKKPsaKYCDWa9VTNaN82jTZs4D0P/xn3x45iAe590vv/qVyqCXE8dosQj0Y+dVkWQXygLbZvoZPaa4vKdyHaOp8UTO9wcqc6UFIwCL9parAqCiGyiftwDopPcUkKw0RC8tKoIz3Y4s4bzK+CtGcLlBkvZhqubb1tQAumbeJnfPAbqxT2X+PBQMNwvQz5XHrJWlcSR4gJgnTeKOOIXkC9k6bHjM4g2ArmOT3AROk1/YqIk9lj5yxuI0F83UB381JOqMS9NcxR12K5j74JZ9QWSwa7JJfQ6Xgh4/JLKyINLieeZFxg7QdcU8ocvZmGBmAlCEpITr9BYA9JecvDMvQgZMiq8wFZx/ITcP7XEuK89geQ1shBGPH/XcSTv62YlZPA+JF89vRbtCAWPngcVXFEUUNHlMoXzZKzyM5Yh5t1HusXhqsVpN2Jx5tT7l8F4w0Bms+2fWDyhxP3nGzvV4DRFZnCbSmmaykI6lPU1lpUYvuoFzZM/L3ZYsKEDvSJW559OqDKQum72BXN/ekWaWSdfbqEYO0I/N11WtM99kbxHZGVMonsrlQaY+J4/topSZyo20JngkT0EHtOHgkV9S1+BzEE7iGkG0EFIUViwGhGeARSE7rQHomcZMPgNxsVtHJZXJxx5/ZKZ4F7HuBQ+WAHps0x4zipgWu4Q/IJBbiU2MRJv4q+8fgI6JlW8AyrB57AanxIjJADea3Mf8c51jnynLHUC9kIl7QdRjC2ZwFBhUheGO78bGG72J2dxogr7BigI8fmW+rrG7N0plMknlY4/N1tMaa/cj3y7y6cfdHC72VgeP0YNOS40WBZnpnRhQhs2Ovm/imfpz+Egzk/Ubi47kWtfV2oA1b0DG3iN2AtB9HN885nD0nnv/e7QCsTEQV4NBX8IcrmV3Yqv2nPP8AH2Xw7Pt8T3vMQqSMW6N96hGaj5m7ed+ezaQyWeItXv4G+31+TzbsNfu3K5mn5jtsV67VtStoCTw/UOFkMRmP/5avIrz0FlYBecUJ/h8Lct99Jpzl3zdIjGd83Yp/h2mG7BeBugpigSX2auM3hUGCtBd5q7PaZeHAXVaEERk+YjIXQ+a+SnewvpevO2Lvez/Ze9NoC3LyjLB/05vijfEkBGRERkROYPJ4EQJFjQgVVmg1WuhvbpXIVpMKunQUBYt3VWiFoPSDtVYtVhtVYpaFgmKXXbpgi4VbBISZVJkhiQTciAzch5ifPHeu+9O7fd//7fPvjdeDPe+yPS1sW+uyPfeveees8939v73//3jR8cssFcR9O0RZVNFXcBY6E6fmIOM8oOBG7K+Yd+yc5ddtbRkcy20mGw5scY+cc+JE/blxx5zb/iDp065jGfLYXjSWQkexyN6Cp08IMO4J/TdA9/u9uwUumYgOik6pSiEXXtTQBMRhnVPufK9QtGVQewrXUvWDldgqJ/GJiMD3heOnBhL1o3HPsrR4yJQCPq4iD3Bx3/pS1+yZzzjGalvLy6HXPM777zTr3z11VfbXLRKmGQoEkYvOnjIdyiGsLG/IsLavdAQ+iZ6Pjm9ClSqYU2n4KdPFsV8UJyISmCn141QOJhbUUF9JjHXYc8Pc9B7qLjO8mixQeo6DCWaakxbo9aw9T7aB3WdUEL1nG1O2TTM1/E9p/juza9ZP/KC8ZMehoabEZo1mhPQ/g3fQ8gnBN/9D09WOOnV1x4IDzrwqUIBpdz7PUnwqYVGeMyGnxnvXn3o3ZMcxJTSU6dJLnk+i5EcxORByVrv5EWHhgpiKbw9vHIk6wj5Rh42POn8Hd4jCPv5VpMedC/SxP70L//wF8YW4pp373nxCzOCnm0dukUpsIEL8EW1dpAZ9ENHr3Tkmz/aXrfj3Z491kHRuL4dR29fJySqyk7DwzbkmDtBl+ecCgDaB+Hz7c1GqtKOe94x3bJZt4bze64syMgxgnt6KkHQSdTJQKnDkqDBA4UXokt+5EMfHgs74YY2a8pBh7LjHhmNJ8IBjz7EInFrqHoORSzaoSkfEl50KEuoMry4rfImQalxIo8cSoTFz5jNX0IPOnLXvVWbm+SrsFB5XY4/zPB6tGlDuzYozTjXrj1mC0tm8ztZTb6nKu6xAELH5zSPdmtaMynnIwj6Kjwum/CgP3PPZQzD9WiJSjHB3wo7h3yR1xnjkNGL3sO+K0xI81GxuHjMlIQiTiDojbr3ypVRDOfC911qpmvTe7vSZa6hiLzGAMUPCpxABq+0AAAgAElEQVQrYhHp4bQ7whWlWGluYXbhWBI5VYznfZJc9W1Sgn4QBN3NqKxqjjl3WbNmi3Wzy1sDW6qj/RoqUYHeRkLNVNNq2Cympq3WmrHVXs1O9WrWbrft1MqKLdTM9jSqkHwQTXjfZ5p1b6vGSKCarfXN7j7Vt2PrA/vC0Y6d6AzsZFRrr/siqNlUrel7AwUj5bH61kN6qghliFOXbVNY+1EI042RnqrAvcHJbr3vSu5Cq+GG3Fu+MRlBf8ZT9nvPd72S+SU506N7R3WAIvCTgY9klGH8wAnY5D/ptQ7ZhjzTqBuCU3podWwkyYM+MDuxiloGAzsFT3q36kTC/WbA88V1khEgi3Dy8HHIZMjjNRjnIWv6/r35aa4j3DVy0D9/23hVoSXvfv4nzG69nWHZah7gHtnwdmOuo2gcKqkjLxxE3T3ozUjZiQgiEHeOt9p5fb2EV9htNeHhxnEewq5UAhhrIupHHnRvJwmjpqZctGGD5xxeXPegBxF3kg5CHoYDGBCUg65INzegx/hg3ITPBUXiNkPQX/BjHAfEDorBgaCjQJwvU1VsR+43Qtvjn7dfw3sZHhh/KhAaKQXAF8XZfG9AS7oo/ol1pm4inajM7sYMEO7AA4X40FAHRSZhSMJ+Abl+9AjbreH6MPLKUa9oSF+ZIMWokr/AwqOXXUPjigwsHg2DOdc1u3nCEPfvuWy363XSseRIyX/SmIWCytQx988veOolCDu86iDnqBPy0MqK3XXsuO8ZJ9bbEeZOKeOy2lNumB6iLiAg6JBdrFEieYQ2h+yk42JDkZoZ164MtNRZfK8J51BSRL0YFNc34+lDCIUS4wbKv9Xtbj92aiz9pFpV5bcnAoFC0J8IVDdxThSzevDBB23Pnj121VVXeVu1Xbt2beKMw1/VBvicffvCMxIbtHtjoGSxuq1ygSEkWbG65qFbCENEeQwE/cJzfQphcuFdgoDxlhHwsNenuDFCS0gvKFBRbRI7hWvC/JAmghAtnvs867k/vUHH+gZSL8GI1kbwrtP77rnl7iKEwjbFMOPIDXVFz9vEdNxLisJGUHnbfRgTenbkyHj5NsIOBB0KDIVt7kGvwogUDT1UzT08sHnQNj3nFNpVlejQN0976hVRTzpeKGCOXFi+lR7AMNzhitWu7GU9nBHi7p7z8MAgVBVeZoR2g6Cj0nSz0SBBryPMtL8pgv6+l3yPzUKT0a1sMLPpsWOhJxgvQMCRY3qk3bEj68g3b9sDq20n6AhxR2xJG62slG+OMGOMH/1rnWibzYTSyPDMmrdPg8d8D3o3Nxq2d3bafyLclhX6oxtAeMNTSHPgXKm+nIlOyiMcjgSdUSNedRW9oNFvvDew1/75zWNtgGci6K6oxvJRpATyz9eWSZbbJ8NzhNBQeJJQiReeHFR3nzPbuUAFXmGjUEydmO8ym54zW9pDD8VUVH/3x5U9M+fqfbMV9E9fNTvxkFl7maHuUPa2742c991m86jinjyblXciWe/Pg6BDefvahEXirrtkn4feco2QJiklRQRd7+s+SbqDJHtBwq63qsHxuSedESlRGTc8JCz+w1BqYIbv4BfKqiqn8uQ6zsfwefekh9LnaSWIbko1K6hWqXCR1r7L1yDxkLtS1PRTS2uzBJ1GUBga2NLsqumG7WjU7bq5ul3SrNnK6pqto81Qr2br/Zq1EWnjjBKhF9Ne2XsZXrr1deu212xHo2YH4SmHMbiHsPKat+9CTvOl8zPxe8NzzG8/2bcj6337zGMdJ+prMCpjnTfq/r3peosE3RXTgcHs6wXNZMD1HPyYc4hA8+ggEHsUOmShP8g6fLsRDbX7ta7Lw0Un6H37b7dPFuJ+3TX7XWZWr6DoEVavJeVPN6yubhhy41tUlo/q7fRQcy+GfPb9GIo70pMUIRbH4nO8YNSUpx5/e4i7CDo6sLThSa/arUl9BykH2da/VEE+bsQ9m1EIbnm16570tfUevYQefYBw77pj99lbx+urLHn3szeYfflW1sJwFSJaO2JJKXQa4e8g56hG7gQd+ICgx77BkPxK3gyH/Ia2IXwjqEtEXqIO58MLtTY8fx0EXefPqsV7/juM2xFmDwx8CUCGgqAj/B2558hVD2+v91iPziluyOvTI79nl9lvbKIP+vf8GM8DlWr5BMcMA4Py591L3Y+2aeG99tx+eLRjHvo4kQYVnT1wjvZahPOjF/m02eJ2cj1FrYSdzlZPkCgrHcDlbsNs/z5WuV+aZooVDLuQ6w88YHbkCCu6I11Lxgs/b1avBC3VUHgU9VQuvYL7Ewg6ngfOgxcI/s0TFon7J4f2uSzHayQjJWZ+xHvSCu/v7ZqZtaWZGWt69FTDyTn2isdX1+z+5WWX7Sg46kYHL0KIiE+m4SH6VMYHOmqYjqPuId45B7pYC+eWeY+hBLKVj6pP1ftKoQzzAtl95fQRQY8zcU4P7K4TK2PpJxuob+WtC4hAIegXEMwLcSqQ8T/90z+15zznOe5FRyG43bt3X4hT+zlSftye3V40La1Tb/NB8gylke0cQDCiejNIDnqgYuMGPa/VnaCvdiBQzH+npxyeTOQRRmgcpLYLPAq+6Hrtx9FUzVtjADwL1ODNqSDoaKGDYxgGTesfv4J4rGieCcLZaLEwnSu5tOK7NdGv03EP+myd11jrd926/8jR8fJtUv7+Uw5URZiSB105j/GoYocfbavmY88UNAlGJ+jBWGhhpeek2gpooOAr/CJDIfDw/hAbFXXytnmRl5RyZZWHmSq4R9hkeM5R2g/EFQQXyqsT9GbdpqfpQUdbsx+6efIQdyfo3oaqYns+ai84w/tdBXFERdT1rueZH13vegE4hLKjPzLeO4ZChtFeDbspnztC9WFYMJv3UK+BzdbYPhB1B6B0LzWa7jFDCDtCznZMtTy0FuTcQ8NCaeJcjGfqyntEZYRXnc+FLd1EB9y/6LeC+Y8CV/CQske1R6v8bd78z3zs42NtgEME/Rjb1bjxByGRp8zQJzycf14VF2GLeB+5eE7goKgHQXdlMXImUYwIyg9WZ2OKYejgUygk58R8LvIMR3ISJYhEZJFLCGXs5IMk6N5+qG22HcrYTrNpKHOzMkLlszcKEYX3P0WLRFVjTXyv4o6IgE0Q9Kt27LEp9KzPvdjVMkoyKCfuns/nhj4+afSahWLlPWW70SkgCr0pf531HEBSml7bAfmG+L4KHlarmXNfxLwi6CRlDFOMnHN5wdPaJ4Y57cuNe+EfoZSUJ7Pft7smzEE/dOCKFBFCyT6wg626LTXMnrGtYXtaNetD4fQuHzU72a3ZI52+PY70KBSIq6N9HIxTNZsd9G3eera9aXZZpFiAlIRN1T3DizMoyMTwYXjQb19mQdI7Tph74XtILQqPJ7431YAxpO6EHX8jisbTsYKZMjqpKsrJ9B0zxGChHzNkwYIbI9FqMWpXhAd9carh+8Tv3TpezQ2t2auvuDTyQiqNv0pHGiEBiRiRfLtBMGqKuAFVHvSoo8E9lQZYGDe8uJ2IeoOKP4rsKQ8dcChMXbnnqx0Y1CuCLk+hCLkbk7L6l9yTgqB6PvfA1jvEuhcJyPL0scND3754+2QE/U03mH3xqyToIM0Yv+eLRyEyzG1vY8bgDb83hZKHvTStE+00IugBtX8u+4lHz4QnOy017AeR26w2k+rFDpHuXS/i2pJfCkcexdANC5hfEQHgKXeRq4655+JowOgm1O/4rQ9MHuL+0n/BYnM+pghfR3cNqGIg4l4ILwQIjBtO4NfoZcdjdJ0vUrtwn/gc/hTsKTAKT4dOqIiGGtIEsnqpCAvPX270gMFrnkYKpD7BgIC9C/L9kUfMjh/jNdS2zicw60z6s8WY5+fZDq81aza9xG1P0WGeV489cc3sjg+Oh11KDbhsT0XQJWEjfzs29rQfMHVo4GHqyD3HXgE5JAMu9ghUcldUEyNSWW0dhlms77nmVEr3kmfdPehRn0NpVztnpzxVL+2e8nqniMvMs57UgZFozNAb06PJFH9FbUL/vOPY8lj6yfCTLn9daAQKQb/QiG7yfDfccIPddNNNtm/fPrv33nvtwIED3iJqoxd6oY/7kjB6xu6d1oD5Mcs3QrwTKIkXMfJicX3mOMYxCAGehjIQVYPRqmQFyj+KkYAohBBwcp/C4Nh4FMaGKkYWB9JjLsLqBD3aa8Fq0AqCDnIIhSUvqsRqlfCq0xLpimijZU0UJYKSHGQKyhjD6dedrM03eA0QdHirHzo6Xr6NsPvRpyDEnWHMqhgOJVmb7PDPiuSx7EiWZyplJzxlGJN/N8KQ5BlNttMImealoleyy2aqVtx06TFWxXIqChF2GJVGRdq9gqjncYLYMg89EfQ60gzqtjDdtAaeuxN0s+Vez/75zeMprBiVsCNBr8I+fcTJXE2yi3B1FIG7f7XtRd9QoZ3ec+abt+HRRHVTEKjYGBEmpuJ2KHi3AENSbeDFrNBJoGk9v8e901M212jYwflt3qJqwQu1INKChNsjOZT/nirtsxezUgWkhDKMLPrYx08tFljBVY0VG7Lm+s9/4tNjbYCnEXTkDIZis3wkiLgq4UYvW3gRQJqlYyhPE+GFs8ipDCURvYAHCFncZra4J4rBLQSnEANM2u2wpMnjYgCZe9BPmq2c5Ji2X0ZvPMi+e7UybxZnb+S/Re5hpTiw7Y8A8yJxmyToh7Zf4kqUwoVjkfkPkSEpKTRqIC2GckcF2kDQUSwTlXNRCIih4yzgI4LO+cF1A2UNhRW9mvoIuZZXU9Oe/dIzA1usVyEO2cf0l7yjQ9SYiOrteQcEfM9NC9FKEDJwUoJ++cErfY0xtJL1Gvah6nXd7NvmG7Znqm4t7BGDvj20VrMj6zX75mrH7mv3PPd8DW3W+jXr9Wq2vWG2t2lO0PdPsUe8t7USOUXkVCj73drAa03cjp7NvZo9tNq0dh+556z9UKszHN0JOlKZmvSoYwdLkQdBzt2DHtEGkHHgLvw58M4MSwhPhVHP9zaGKoM0wYOO6IN3fXm8lB6t2SsO7vWoGvVeh2xQnQD3nmZM0eeCk7ZImUBnDi8GF7VBQNCxbn2+KUKHcl6e7vnpmK9Npg94BXxfaGHSjn7oIuWI6JFXnfhkazwIa+pGogitqPCtsHO2ZpNzjrURfK7g3L2+ffnrkxP0LwRB99xoyOdQIXRLjKpLoiJVIFdBNv+ZWbJygq4dKH0cURaQNwmGIOjJK6kwbhiQg/DqpwrZCUHpNd7qTfJMofNB1OHxdwNLdOPAd1Bs7tB+s9/9s/FIZr7Hvux/Y4933KO3ckOEWRTYOx4RTsLyxCmmJS0HQYfBAUReUzO14MT319jDvInCooGFV21XHjjUSVxvJqq5R5E4GFCAqUL/d+0IA0JERqD128kTIffjmXrF+2mzxizJOaKy4H0HQce+1cM/pFiixgCK/sFQDAPEitnxvxoPuxRVeuluj7TKDTiS0XqelKs1W+8y8olRUZT7kP+e7hmRc1hbzDVv+HtoKYk9BHoBPO47ovq79EU6a/peVA7nUYX3vdtmbBaTJfQ8TVC1XeM6oNbIArtVd6EUEp9plvyVM19zXRGzXx9TJx7WCspfFxqBQtAvNKIX4Hwf/OAH7Y477jBUcX/b295mCwsLG571p3/6p8e+moTRtTt3eKh4bvl0ulljiI23rPGfqiKJsKQIr3NPYy1altTcO7LWpccHL3p0aGFEBWRXGCMMURtmvvE7JVVMeGxmzfq0j6/RaHqYIEM5w3uI6/Th7e+FnBlYEwU3WvSmQLmmkGUF47VO21tnwYMOpQ7cBQrvw0fHsxYKux9/apaD7oKuykWXwiAlPPfCjj4sFpCqqj3nrcXorRjtVhm6UOY553VYaE/KBkkH87ywwYzmoFdEkx4/J7bAzaC08hkj/NMr4LfQMq9mLeQ3BEH/kVvGC/nMlYf3vQQ56DQ4xf7soayYZ/CMo6bBY+4l79sj6x1v7Xes07WT6KEM76UXLKSnzJVTeMxrdc+Zx32gPRzuY7aOmgPmRB0hrTumGp5bvmcanvO6Lbam3FPJ0LGkgoWmKV9SFQqtEHdVSaU3tvL6i6hz76v7/Ed7QhhdQOjwOfKY3/TxT10Ygt4zA0FHSCAIsffpRX6hPB4KXQ0PJeYB+tAiTBCh7CDl8E64EjRlNjNPD1UzCFKaqzGpco+tP7t4QzpDG2GN6FEcY0H7G3jhnRQjl1GKV5w4Kdf5+4I9MwrIg45cxUlD3A8u7fKquarWjaEjHNEjLhAqXavRm42Q4JBiqdNBEHV8rgJAIOls08Wwcuabkxypm4KqubMQI+URuUxFtmWYErlWlIYMCaNGA7XJ0hh17VzJQjE7vOj5pByAHL5rwj7oVx680okvXyyith3h4TWza2bNdqIoXIvr765TPXt4bWD3rPbsAVTxjkgqLyTaN9s7VbMrZxu2qzWwK6YZgr3cYXg/xu0Fquqs4n4cRt++2T3tmrdVO9FtWBcHxFhA0PHy/QEyAAQ90llSm6gsBNmNHFCYYXwBWXAPes07N2xDyysQ9RZlIcJvQaARRQSv1+9O6EG//EBF0D31KiveBOLkkQIq5BnechF0elxZNd9TdFrouVz3PRg/MTcQWq5wc8jx+RnWwEBxPZDw42uIhKOM8uMjxF2edKZWhZKugqXZJpX6pEd1d3kDtW/RkMm5oborilwQQf/qN+6fSN79wk+YffUbZidORrBcbHD4UbXR5GC5XjI8g5jL267ZK6wVQi0ZrlsmpsFalC42EkGkkHl1yPC8a6ojwwaO2I/9/awrhYhetUY5bpejEeK+f4/ZuzbhQX/FzzHc3+VHePpXolDcCaQF4L3Yf9FuDsXpVhHCDsMuIiNAqGFQDSw8jzw86giNb4URw/O+QdCxiDSXs8gAPRcZRhDq70XygshDpsMggqgreL69Hzv0IVTmx7WxUOFBR2oBUrCaZrNTzP9HTRPlx7uhL2oHINJq7QuTEfQXHtyXcrgrp0FltdJMx/2o45FH70VcJ74joksDdOgodXRCYKg79gxE1GFfWJiaZqRMTApfV+hOs94Zqluyc2bKo/wU1ee7iIyOqRAmHTs+jbL8dO44soZnxqdkV6vSYDDGrzx+dKz1OqrTlr8vLAKFoF9YPC/o2V7zmtfYO9/5zjMS9Ekulqz7O5aYu+07hXLgKJFBzJU7qzYP9BQxv8z7Trr3Fxt/3dZ7dWt3qBxQQLBKMXLQQNDdbxykWUU48tDv2OHTUHASePc9bNkJOlu3YFNJ+6Wb1SMsHp6UZtNmppiPCMKmcHtc/1R7zfPVZ2s9epmjANPDx8bLtxF2P/ktB+nFyHPQFequfMfM+5o7Jqrw9OgCn+WRjjjaTsuDUsBtpVAorUCimeKYHr282ntFIJL3PIiHh+WOEnSQuchznJbiiwcfBP2Gvxyv+j1GVXnQX+DWYEYfsPgUepm3+wN7ZK3tbfMeXEMBuK49vt6zkz0UhqNHHYo7ZqgiOlR9HmGqCFGHnuD9273eQN9/395qeDXmg3Oz7jHfOzPNEOLwMJKUpSjxqh1JPIyqUBcxZFG9KB6XCLpKTMV50DIltSus1tPJdsf+9cc/OdYGKNxe/mNm9x81g6LlHvSe2cmjJOhoc+Zec5BjKDfw7sDjEcoZCh3hH8g5KrnPLJhtQ7s19Kud46JCbrq/Mq8Utd/hcOo093S4lAEUGFK/3ygM5F6ajIAnnKUcxBu5Jyw9DGr9fg/yoN82YQ76ZUs7nZDjpUgXhCbCOIMqulgTqZds3JfItjoewGAp74gIOgi7XizKSLYFEQM5tM1bE9Y9lcKVNxCkqKzuHsFgDqo9ocgfkWsSIYIlhS//SYgqryyOVX68CtJ5deB+3+6YsIr7VYeuYKh1rDs80jm0GTKzA1MDW2oMbN/clC1ONe22E127b7Vn965iDWMPgBxmHj48rQdnG3bdArzuNSf3IJJHVqm0rqZaEg0PbX+oU/e2ag9267aOUGKs6hRvHVE3Ics9LQAdFxpMwUKofG7cgIHA2+b1GAHglc5RhwJtPQ1RNgOXFTsQDeCh7izKhp8g6DfdNlk7zssv22t9pJExvskNEDKeOkHP/pYhy+dbXJ8eYBJ0FF/bMYtuGg3bOYfceKxxdl5ZWae3bWGm4fv2UVRpd2zZKpDVWkh6PBx9eEOKKRxG8Gy9uuzzEPiqJR2NkvJcB2XJ6ioklzZCv7t9u/WOyQqxvuWnzG6/m1XcYVFnZAuvrbQF7QMe2h6GQEUl6LM0ZWKM8o66HqEQ9kyWRbOctMfkRhRfb7mlUlEGkCtBxLVWdR1v+YZe42GQ8c8D47ClJTxhjEQI+J6dZv9hE33Qf/QtlJ24Nogv7gkectQbOLFKIu6dONFWDrniiAgACY6OHv7MI6xdofkgx16NPaJP/D4CcyfocT6okohQ8fsPtdJrA2A9wRhcN/Nw+z693V5lPyK+YHPDP3UfQQ66E/GoEO/BbdjTIu/dcRwK5aJRoY/aBceP2+LiYpLPZ/tFe+w/uYKOl1QlMG2IJL98qYZJRnZHN8XYu/KdEzLYU0ajKwgcVqjmzlolNOzKiOupcREthfcWplou05TmGXbekCtZy828XS3OlqXoaV6mzSRzpmlOYmyff/SxsbA7L4DLQRMjUAj6xND9//OLEkaX74DwovIgb4SaqZGgR3V3hAeHwkAlLaoF+zdZzb3br1sXkjQrwIQ+4/SMwNPtF3HA3JOeRB3CnqggU1GOwiW+EUdoYDQqZYAlvcHYJFBd1w0Ag55XnUdo0pSHlNL7oSJdnvOztu7F4qZrEa4f7eWOnFgdSxgJu9c/DQQdXozIqJfykFkuRz3ovo9E+Gz6LDwTeY4koam2g/wv1rGPzU+tqjIl3i/gBd+Yj5narmWh7QyZZDgWQ6hI0NEYyvMz4+csQrpRhKmJokXI8aeGc6rXsxs+fttYuOEeEkF/8fO9VSAUxyOdnheAQ6s0kHDknMNLjp+nen1bjuJwyDVXXhsUXd4D2+V5/3JUaXcjA3Lna963fMdU0z1ge2aYv3XJ9JTn1m9Dvlh4PDEubnqstI2fVVoBn4QIOr1FNFLRu0p8uW0P59Cz6jUrz/PcDE8+2enY627+i7Gwywn6fUeZg+5KKBSZVSp/7rWGohX90N0jgTDE6A+slj/IL0eBnW3bzbbvifZr8pirKnGszaSOZAqpfs31e81lubUU8ZoU4ziYoXea3Vm0XmYQSAaqSheqCHrb7LZPTqZ47V/c6SGFhI2h5DDWVL3P2YPWjZFuTJRqVd28yDJzCUnWIVsqslIp73iPnQNA0Nleh10lCDK8xV5nIshO5UFn7jErtociHzNsI06VcwXNVRakq144F+TknRN70C/P6ofQX+pRNjWzS5oDm2sMPBQcnt17V3r2aLtnj7XNjsJg4wZcxi1Ak75yrmHfutSyfdM1+5b5urf6Or7a8y4Npzo9D2l/sD2w5Z7ZvWs1Wx3U7Hi/4b3NqwKMXG9pG3HDS82aTcoz1UlRSkqI00TQnSqjrR486Ni98Hu/b7ONmu2aRm9j83B3PKmpOvevmyYsEnfFgUu9swi9lXxaiaCjI0pO0H37DLniCjvp6LYpesa3zzRs9wJqGzCSzft5d5kagd89GqTJ94+tDbxK+2MrCMM1r9XhhbTCg+5h6NlLaziPcnFCnLVXS1/JSGlsaZKAleQIotX3PuiThbi//V+Y3XmYldqdhGWGA+wFHB/fV3pAIuUSybFA8vdTxEK2j/rhYUxMy2fUK1+J+WSES6I/axuZMJFci7x2hdtLXvj4s1QetVtDigdUs3//f40n6/I99h/fUHnCkZsNw0zKQQc5BwGPAmz43b3mkXuu6AJMP3i03ZARbcxgPJA33J0TWXqK31dECjQZiJk88G7bxznCKNWG0QLzUalYcufH90DYPdcfY8yMvD4mGGuCuOfGZBlOQNDXbxsPO+2xL77yYNUHPfaAnAyH5BnKGxdvr8yoclkPLbGIlIy0Hk8bpbFf+hzFA1NXFOniajMMIp7qwj0qRZ5mBl5GwoSjYaONOybl0KpPht8Yeej7f/PweO04h++y/HWhESgE/UIjegHPd+rUKe93fvPNN9sjjzziilb+2kwO+kEn6PR6e5suD9thqTZ50Nm7vApt9z7m4bXGOLyWe63hXvT+gF4i5Vz2QNCzEPeqvnnVxgKeGVQ8hndrvtWKasg80jcOCPmsCBfJJwlSt9dz7wbChlZ6HffyIF/d+5zXOU54THAvJ1Y7oZRROYdXHuLs+PLaRGTpDU8/lHnQScJSmtlIeJHCjRSiX1VmliWdWnoKG3RBTaHJ+IDK6xf1ahyDpBUlwVwV3IPgH646Xv3tJpXAUASdVnGGhjMXnW3WoECiUjCjDrhjgUDf8InJCfrvv/j5Vms2XSm/+9Sa9zK/d6Xt4e2oyg5POtqptWF8iXBGEhZ5rtUWDmOF0aUKWUVlZhR8Qmu4g9vm/Odlc7PuHZuDIShTJbVZySqtftjyjhNi9ibVBohjlRemXNBKW4utNrzqoTv6FUU8l9e79uoPTVbFHR50J+hRrdgzRuImPCQx8u9A1o8/bnbqZFWEx8lzn8R8YafZ4i6zSy4Lb1RMXD+VCv3E77msGSWD+iwR9Jx8h/fElecAQngkkj5CzIcUoRGCjiJx3U0Q9H1O0PX0aeBBr3K1MssjV/KcdCAkQp6nm2B4kJXwpON3tdHLU3yw/tB6Bz8TUW+ivCY8ViTovG7V6k3XEEnPDXVS1kTe5SGXp1hyZbRVI54bPCMT56AfAEGPdpDB0qbDwDffGJDMuuFzYI+u9e0EUlG6dVvrkpyz4nDPaoOeXbPQtGftaNllM3X71oWmdbsDW4a3tz+w452BHev07WvLXTuGvucraOlY80JzbpSL1msqfpcU2kidakS+NrowwKgIowj7gfMFDLzDCEhqv+8EHZ6+elBvtlUAACAASURBVH9gjX7PjXl7ZxGBRbmCO4b5Gu1Db/r6eBFDUvivPLjPo4R8jkTFZm9hFnnHlCUk1/LUsn4IyA6U8Z7t2kZivnNb0/YvtXxPheccRdnaXUQEMAedBh2mnR1fG9had2CPnKInfS3IF7zt8oYnoRREi/tM7GPy8LoHXa3qOBsr47IMSAFwEhAwgNCIjnu4997J2qz96hvMDqNtJPqSB0GXkVsEXW29qh0ztkWNZZSgB7aQSclNkOWFhwXP5aDkoYq+DXnis+1Xzy3Pdfe9Owi4tmp5+N1IL2NDxpicoHeYp41iar9603gkE9dMxQn/++jOAbKKVmcw0oCUh17lyzjaZioqQQsF2Wf+L7q6iECijghaqzk2YZBgREZEZoigA1ts3IjwiAKmVdQjn493Z4njNRR/HiDfsZd5sThcB5l1gZNXno98drW5S3Ixnonvg98YDzvh9pKrDlUh7iN6vPYvyebh/UoPsiK7o3NSx/vWGmOFbsG5wp86C9tGVpNYOjcIem5bo8NMkS/afWTg5ferfXu0FkquocRz7Pft0w8+NJZOPAJT+fMCI1AI+gUG9EKe7uUvf7l97GMfs1e84hVeNK7Ki+FVNpODfmDHolebdq93aNGqJsxQnCpsGmGDUCZS6HpsMiyMgUpTEAMMIaWiQPOp56KnnEj6f1mwDMorvBV12zk7430gFWqqQHlY+1kcSeHDFF/uZU8EvW/LXfTHbZPI1knOp1Fq1IkR2sgM7Pha14U8whpdGWo2fWxHT04W4v6/PuOQVzjPyXPuHdQccONCSGYPy5NH1kl1fDYSUkjFgJ55t5hi34LFNfZU98Rlhebk2dNGhQOVYz6q8Cvfia3VSHjdIwyPWGDrnnT3+kXrqCDoykMFQX/tJ8ZTWHPl4bevf56dgGLe7dkdy2t2otu1R9eZW47q7Wjxh5Zq+Cnyy6iJqjghir9BbQchB0EHAZ+PSuy70TathVD2Geaaowp9GB1EurkZcusSQUdoMH5nPnG1iocNKvDaK/KAOHGmDXvSdYKkf4W2h9yzV35w8wRdbXrSKGMOIf8OnvQVVFJfy3LS4UVpM88cPcnR9gwedN+ilZKhzTxTaodk2ShDj5tLqkmubMben2xNWZ5rrtj4I87mP9dsvBeaYcpB3wRBP7h9V4rc4WlR8brhHu2KKBMArR2t28o7EQpQGNOYAiQPehXxQmOdB3Z73rt7Nj3lpPKkCwNGbVDOuUc9IjHkxc+96ZqrPI6CVsqzy5UIn8+JP26IOeiTh7gf2H/I3V/u2YmBo14FiORck/UqVNUP+eQIW/d8Vi9KhT0DuwyIZN8OzdbtaYtN96A/dVvdw1kHPbYpRL768U7fvnoC7RPNvrmGPug1Wx4gbBsUn/OVMk3xyhFjG33C8Rk96ObPF+k5fJ4MsfcaHx7V0vcQ/TngA6NbrxdRNvRQQ74Q256td7v2u7ePJ+9ygg4Puj/jkBOs5A3c+FMEPXl2g0T2ej3Dvx1zdds1T4J+6VLLjRqr6z32IG+zJgAMEXxxT0GrO89Bb/OZIQLBI3pQuM17fSP9TPt0RRhpgM/yW6PQq6JfcllIw7PkngitpKFZ07/bt/vvG0/hF3a/+r+Y3fcgCbqquEvEqECdKqtLZlTuyARHClUXvvIQRwmDpLOcFrqeFc+V1xjnVwg90a7SrTQlpQMJR5FLXd91hkgFctVIchTPaZ1tzFDl/N/+3ngkM99jd74gdI3IJ5fhRY4ExwvEl9knsa54L07Oo4VnIwp7eoh+k7nmrBNRhZ0PRTLoZFG0FLIbn4tuJoMyrgvwYl/AddzjHgRdBmUn6DJER2u4Bgh6eP9jyqfn4DISrT/vHg87zbl/fDmL//IZDtf/0V44RNCToldpAJoXaWKN7JOaM9RBuOFp95AOxwS6mGC+nlHzBEXm2B4YujJIPH4iMkxG2mE6PrxhB9yVypBbpeNdGDBvvne8lJQhHaH8ccERKAT9gkN64U64fft2+5M/+RN73vOed8FOKmF02fZFr35LQUApIs+5qlDCG63cc1f0EAbqig4FtHdEB+EO4k35zP9D8VGYOY7EtSDcIFBAELdPT9t0s2mXzm1zQZPMhxHaJy+TE/WIZWN4KsUQ80EHdmy9bY+3V9neot+1KYRdoj9mVL9FqB/CKGHVn64jBL7uVfGxNz56/ORY1kJh96ZnXu5h1L4haJeJJyQlwo0pssS7JzYK+rhXTpsTD/ANOwrXoVieb+Lejz4KKPWZOw/FGFdNHvRMsEq4463UHirGoNB4kQ9GIVB55e80qOvcHtrumwCU3WhZF+FYq72e/cgnbh0Lt1x5eOeL/qHdubzm1dhvP7lqJ7oIbaUS6TnpCOWKkFBWLWboOrz6CFF3D3/kmIOgo43aXLNuO6datmNqyi6fn7PZRtN/516baUDceROh9lD0IEltDzuOmL0sfcDJU1Tfx9cTXpEeIJ2EPyM8Oo9w8CHw/bVOz/75n01G0H/wR83uQ5s1eNBDudIerhBOkW0vGAdFBgoPCPtJs9WT0Z5mzmx2niQ9Q2eo3eKQJ/08JE8i3RlJT5pfYvBxvexx5N87jbjHzTlBRy/kTRD0q3bsHkkeBamhPFJhOF6Onlr6XTPSHQRYBjN1mwDBHn1RYWVlPhBznElKnTzqKEyH9ylvaYSTQUDjwDP1Wgk4R6SooP3laA/2FLEUHnmeLcR6XBsE/euPPzzWmpWs27fvIGu3R5ExoII1CJxmWnVPLXJih0v26GFdW++7d7eD1Kc+jGt9/7d3GkXi6nbJlNlVs5DHdZv3Lh0Nm25Nuyz48vFVO9kd2P3tuheHe7zT8BzZFZDKVJCvWsfeycGfG5XqVgNGWkRINGymySKQwFsgI7wdAhcpMWjFCA96vdf1sSxNsdUiwvf9+ffRVq9r7/rahAT90L5I1cqioCJcVZ509s9OAieRmU6n65Wil2brtnNbw3Zsa9repSlrryMirGtokXZ8pUtjN4yosZg9XQw1PrzwIVvSARzMCt/XUbSyjegy3F9EfwVRZAX88P5qPmV9xN3grvczw57vV8lmQkMTbOQg6A89MBlB/zUQdHjQ0X88CK3mtcu2yEv2fGl9LotVyA4asqqwbJEjL3iWL5KNZJxUozinCp7JYz60yLLvx/ZSCVdtEHGMy+gYswrS+SHhcQZB37Vk9ut/MB7JzPdY+/bYzNMGUQ1Qe0UaZ/A45ch7Oz/MSZBy5KWvs20m/DA1FJ6L8PJYRiT3cXo3eqhgm55JtM10GONgNA+qQhiiOFzkvWO/gqfcO3noeVIhcoMewuf9MjIkB/v3+4lifcsPjIddarN2YF9VxT0r/pbsgRsQd+WOJwOQnCdRpDOfWqL8mpeuQSYdsVJV9HlIITvVWffWx8faa97uc6EF3RlRpyh4y1CIIQNT3js5zfNsc04Rn8MkHqlXH7jz7rH2ifNQD8ohm0CgEPRNgPdEf/XKK6/0nujXXXfdBbtUUry2L3poOb0RJNMi5iKGCrejEqFiMawQi3/09MgzrlA+7wbtpA+7KJRD9x41WcBNP90CWK/b/vl5FzISFfBuQJQoL1M/KV4qbR+KAsZ7fB0tuNa8ddqpbse9JyDo8G4iVw8hfiDo2FAag4a3e5uGAmNmD47ZUkLYvfmZV3hVzfSKjSe3UiYLbAj63IPOyrr8x1A4ecxpTVWroPUg6CACzIkMrLXZOyRSmqo2TWz9QenvhY9jY8HTHspBF0EP4snPSNqh1CaCrt7qNfOc8dd+YvIq7v/nP3qeHV5p2/H1jn19mQQdReIQ5trJSDq8agif9efoJJ1FmxBeO4sKy96rvWaLzbptazZs+1TTtjWbtnt62nFCAZZcX0t0K+1kKvLHuQ/iI09mTruU28UIf3rNlWYBfDwMPp6H6xSR5qHNtdIDa7ba7dkrN0vQ11nNVoqWP+VsnxXZc8IXHpDOKj3qKAqHiu3ohT6N4nBaUZmSkGwLOmf8HDW467j0/kj9BUzuZB8ZsZGkaBOtg1gMaXXrF8ic6IMO79KkOehX79wTRhlGSOCV1yGgjY0zpMpBr/LAcTxaT4JQ58Q9107d+BjFKdkuMb8W16Ny3n0dg0DBKJQZ+DylRVEcPtfYZUFh7aokn+ZURMF4rnAYRIfJflXF/Y4jkxH0vZceTJ5SeQXV7mu6hS4bVYFEkl8Yohi5hMJTaI0GrgM1clerZvun67bYHNilmIdYwy3ImobNtqZcttx7qu0Gu8c7dfegP9SueZcH1KjwatKZhwugujEy1pwbMcMDOItQd/Q3j84jIgiQhfUBCobWbTsMJWjX2EVVZRaYxNqeQci4V1TuukL8WxMS9MsPgqAHPhFK7SkUUaEd45WHP3kZg+gifxsEd26axd9mp+r+E3MGBhDsa6vrEVmV0je4TzcbKKjHIpwqTofF6K3A+gNro/1a/K7CjphTNEaS8CoSwwllrNONCDpHkLV1clcoCTDG/8iDk827/+ONZg89Qg+69sskc0KuJW9/kMGcHCeSkxF0qQ+aCzmBz+Wpy9Rse889507Qg1jlGkkl54N0ZQQ1bfZhiNO4k2FhhKAv/W2f8F95z3gkE9eXflJ7Fgm1v0RkY5NSi9BMlaLcE06hu9V6JOgwhnioeixi181C0ZEcT1tFyHH1MldkQ4oiCLnu3X3hk9GewbYs6R+IuL7jqlX8rSJyuC2/ndBtctUQmJ58aDzsqiJxB1M0Cu8pr0cScOYPPzNO5LpG/t2MFucNdivdxPdeHgUnkp4ZIyPpoDm53vZorWPrJOjzLVR1b9r26VlP1ZKTbEhH9gp+mpVVgmn1ztCM9T8gW/7rN+4qBF3QbIGfhaBvgYdwpiG8973vtfe///327ne/2+bmQqPe5HgljPYuLabewKyoXuVCeq6LVwNXSPkwCUCvTCgIzC+k8kkPO8hi35UOkCoXMv2+e83RUgJeq6XpWRfMJ9rrftyhhYWoZkkvgAphSOEEwVefbwpmjhVKMHKZTnTW7Wi77eT8+PqaexdnWyTni1NQaBDijr7uEEDsx76AaicDs3uPHBtLGAm7t3/rVa7EhQjnz6xKLP6uwlOZP51y0FOuH70VyGJFOLcr2VHwDcYH3D96ZjqG4cGdlkElKUwsOlIVOIuw1wjT5n4Z/ZK9nJ8KETFkPOWiZyHgQ6HvEQLvYbrh9UNu+I9/4itj4ZYrDzde/3x7tN2xk52u3QmC3kFhqXVXztcijHYQhQGn3Zpfs1lXnkHK607MF5sN29Fq2lKraXtmWh7OujTVilD2PH9rOOhryFodm5ciG7Dx+ZyKOgzDWxoqKcPTx4J6+OepBpGbK5w5D/i8K8pX2ZVWu3179YQh7smDvm6mEPchq3mujCmXPLQGKTh+/9q3c+P5yO+5cpuLm6SQ5ccn7y+PzAm7lCznoHnOuR+oEL+RXNbsOIx3yIP+qckUr6fs2hv9YVmki/SL6gzXaZCTGDz+ZipO3BNaV6H/eQertfqee8jj++x3S8OftwyLau0y6ig9CX+DOOK6MgjJaOA50kFCvVZEFCSUnIEXhUYCvnwORgtK/268Lw+/5BJk8KQ56Lv3HkixsFoTKJ7modUtFpJUVAqipkDSQQAhd1dhXOkpPaXu9SF2Nus2hf7jtZ7L6hkQ9UbdKxVjLa50UVvEbK1Pgn7v6sBTX5a7jDwiQapC3JEe5VEPYUBDDi9TdBjqDoKed7XwFmuoQVCv2y4Y8UCEO53UGhTfXWg1/T56/Y63Rvzt28aLGNI+cfAACHoUhY49ggS9irShEZxRTHmBSkWAofAb/ikVojLAgABzgmrN+ZaU1ZFAz/hYatVEjt7zzMXPUkrSGtiYnCuU2ffliAKT3MNPT/YIQ5fPO89f79vjYxadEnb/7l+ZPX7EbK1N77wTNd2b6maMGP6kh2SisCKoWsuSL5HjrO+oEnnax3V8XEuEXR703JjoY9Oi1N4cFdETOc/GmohpboiUB71pNj9n9gu/PZ6sy/fY1ndXXTmSzO7SK43oEHe2KN87C7P3W/DcusqL7UNU3nmDH6tgq0ipT7MESFZZPXuPc4IgNaLNmhuJcEnve0sPuZPzcLAr0sAj+wJfDzYJD7rva6pPoDnRNzvx2HjYac79j9dexWib7HWaMSZn3ENHDq200z7JNZHhD4Oc+5uaRdxlENoOuX6svepySAR9WwsFbxu2Z27elqbQlQah7kyl8W8OWwVOv5/0zrDnHUaA991WCPoGD+/v7K1C0P/OoD/3hb/jO77D7rzzTl94V1xxhVe/zl+f+9znzn2SkSMSQV9cdGJDxZHKHRYoreRUMN1TEsqPlEgcjV7VTtAZXOhiRYoiNnAn6MjPjRx0CI/F6ZlE0HGNI6trfu1D8KA3my5wIBplua+IelW50i31EXIMEouN4lh73R5vt20lEfSaE3QoaEvTDQ+DP7kWSl+PytquWdQqH9jXHxmv56Ow+2Un6KOCPLe2Ri5zFDcDxEoLUJ4ffnqlfBTlC4MIj6FHHPcPMqzNHOdo1VSJOkLJpCxF5RDmT0cgVcbeXAlhxYFELPNicfT+cfx5GDxTEkhGVXEURPqnPz25B/13rn++t0zDee5fbdtyt2sP4menZ0e7KPrXt55XPuYzBEGHYg9v+VKzYQsg482mbW/BY173n6iRkCplJ9fHCItUvmRoCfpU+ZUgPwj/RPQIldCKQGIJMX+fYctqhzVaxV09yRRup/70eIa4HjzomyHoh1UkDm2FlKZ2+m1WVcilsOdbf3Z8rnwkT4s+z8jpmYSMK0u5MpApockDEkD6R6O56EEuTvPC54prhLij+N3tExJ0eNCZM0tDSzVurFP9XYW0U1XSeuZP9UD324hq/zgXh8rzIuwQbyi3HOvY504QSshDnFe914Ur1171YLQGNed0HIg/Q9zD2x+pKlXrtuweMtYAI+edE7ZZu2TvgSp/PxkjqOSD/LrBI1iNK8vYHzosNIoWTshFB8ZYM2jduIC8SUMOeHQDcY83IqwYRs9IFoRj190A+xiqTiMPM2qRkKRVLBTkXik99KBHDY0mjbRqCSqDmrdiRNsy1D9B+zs8k27XDQqQRVhWLCiJaAYYRLr27glz0C+77NLTvGbDBL3qtCFPumYBjBcebgxDQxOpAjQecj+ocr/z3BSkpHFNBzHPImN8OgQ59SruSq0a8ZCrqCkNaipkGKQo6ivgGO1n9G6HB30Dgv7YhAT9P/682SlE/kTP7SH5NELMRY0YKcY5mFaT/s4rrcc9u3iU5xgh3NkX8yJy+Qm9JmEsyLRHxN/OU2NsbvwI8ujHj0YY6djMcIBcbzxzhLn/y38/HsnEJaSftDIPevJiR4V0NwQFKU7jyzmdxqww8yDDnnse5YYUfY57EkkPMUioRoBJRoLAxu3vMBLEHhsdcyuCruirwJBRL/lDjZSKjKDrPjGm4xMS9JdcedD39/yVb69n4+ZVFZqRcPNsTqSs9uTcjjMm5w6v5p5zg4OGBB0edDgQ0H4NespUnR1I9sxts6Up6tUg6SLo1Zg3+i3WR9KDqtmM/eUPv3HP2M6XM+kH5f3NI1AI+uYxfMLO8Na3vvWs537zm9889rUrgr7geXr0IFGiroYXsT+g5xaeHvVlrchfzdZQZRtujiDokGlQwNwaijwhKFxRgKnd7brwWJqBIGm6QAEBemB52TetA/PbPGfYizZ5QR3eEitWsqCPFAWG/UQv4Wi9daTdtkdW15ygn1hvu0KDcyF0csdMMwQc282c6uCeGnZwYc4VsM/e/+hYwkjY/dq3XZ1y0EcfgCzwyRsScXfyikcUKKMN4D23gbUNhpHKi6djvU1XaAcQo3UR9NhQacSQepJ7/Ya1FFf0gygojJ11A2heUYV0hYqqEJ+HSwZ5YDg3Seab/ma8nMxceXjfS15gMy0U6UP0Awh5z+5eXrFj6x0Pbz3e6dpqPFvkliM3dOdUw5ZaDQ9f3z3dskUn6PCYsyWSogw8pB9VZVxLqFSHIX0hNiYZnBy9iGCoCilxjsk44rpBeOhE0KlQ04PKUDime8Tjck2N60j5pzVb7fTsNR/68ERz7mU/YnYYOegoJNSsCLrmX+5NT79nymn+eZqzKoTkeA3PZEXbnVEpySPoqikYmnzlUTmj5zy+489mROFO5D76oKuK++2fHk9pTcW6dlzi6x/zRB5z3ZdC3bkeomCW/OQhy0AA5cEcXe+q8u7VtDOPJe5J+eUqRKj8XRm95P1FZAyIG/6W11wROBFB7CMC6QWBlRxUWL3G4P6TUGRVv1ye/G9MmIO+a89l1vDoGRrLXFaE0bZRJyEUuURBOLyDCuIg5ghzR7SVd/tA+Li3Z6unkFWQO4Sv0+/DJwKvK+dEpKBEbnlTxmTldgZRxx7FqAh+n3n73ANm4Xn2yYQoLka/ILILhoJttZrtxHfRRhS9xLs9NxTyHCQxyJuHQvxf7hiva0WVv39pNSeCdAi7RAyjiJ2iACQ/YFxANXrsZ8gxh1EceecyKLK4qyKmKrGjdU6ZF/NB6ysMuUMkMsm/kUiWDdalIpDciOxdVgb+jHktp6cUI7EnoRXqIw+N17ZJ2L3nl1mwzE91HsbCJAfjl2RnzHN8MzmVonvOROh1wnhuSQ6OyL1cPhIBvhzjaDuWE39+mB2XyV0QZsw7hJT/0C+MJ+twWmHXfFrVNk4kPMfHx4lc7rieE/bMIO07WoSVx214sTYv2KZ+7tpU881V3u/RvaTioWl79MfKGsU+Di/bIQ96nNMNfvF+2hOiTbkHQEThOGEaviY7NiFB/weXXpK6fVR4VTdTPevKcOX3EXNCepN0JalmXCE8j2ObHZ8MTy5H3SwYqab9yD1HcbgO20SGTqyaRXtm52379IxHbsE4LD0mzf0wDI+Wu4vEl6Fnoev+yd3jdV0Y3Q/L3xcWgULQLyyeW/5sEuIHti94WKErqOFBh5WOldMrgq62QxQsJCMoMAPyyPB2hoxCWYQCCs+6K0goxNbH3yj/VfPCbM0aiDN8Jwhxb/tmdGhh3nsS43j3SLpiRiJOgSOvgdqZ8T14VdzDsta2h1dW3biw3Fl3JRLeGLTb2TXb9PtBj13kQy530DKublcsogOu2V8fnkx5gAfdK+duwF5GCbqTxiy/T6QPCHsUABRvY34l/vYIs6g4rKJ9eJ+hsQz08kiHpAUwtzWRbO0Y2UxU+Kc86NVPkpKKYKoPc4T+qpd6eJBxJIrEvf3zXx+LZObKAwj6bBD0tSj09+DKmld1v29lzUk7eqCvD8w95IhUQE9zD2ufajkxR6g78s4Rsg+PgNT7SlGsPGx6RHleOZ5ITtAxPuWaq5NBTs4JKbc5KvrKew0Ng/7WKnExXAhKzVBrFBD0V3/w/x0LO61XJ+hZmzWFX+ePe8h7VO3SQ56hnIhLWVDEsKaMe0vijzMR9FHCn4ezp6mZh7WLJIwqqDk5H71YRtDhQf/6hAT9Cifow/ndNKiEB1Oqimuuw550PG9FSqiCurzdnCMsYql1H7fHIlYhu6RYKQyd8wfGpajynuo9sICmSHoiYPGA0JOb3QY4X2U00hjykWt+u+zp9e32xycr1gWCjpxmXEsE3UO0PQSfoe5Yh27ccLMnCDpkFHoZDwwOfxr+WOCSMUDEGYomCsBVcy2qiKvSNY5T8TdvoSlDmLxUJLEsmimCjnBPRFExB92jw9CKMaJfEN4+V6vZtroIulkLhdO6PXtgpc1aJeGJBkFHO88/vGsygn7ppXurkNm4SRqEaGBzORxt13hvsehQbwMRALg3L3LHWiowfJCgU8On6JYMPz2IlpdU6HlVtT0R9MxL7rJzJL1kNFRWkRLw5oOYY95jXPLqi4DIx+9F4ibMQX/vL9OTLINTWlfn0LAqsywPzA0WQ/czImtGSbSmlB6JDh+Ve2eSjwrByh5pGvmobFVFcyfF2N//VtZthqC3npIZcBX6PZreFIxcCWG5QdYjvuSdxoBo3U+V9UQ8VXwlGWWCh6bUIBHzHETMMXnp9bAyQq7rpuuHkSN50EPtgnHPv66NXfsI2gxOSNCfecmOVDi52ktH6e3IBEwdH1SYONJ+0lJWRGOk5oSsdNEWugsinHA9tQJFOhX2DHjM1UoYx6iFpvdJN7OKoDfc8cRaJIg9lB5X6XPUQ7kHcn0SOD0a7CnQff783vFqRpxjOZaPN4lAIeibBPDJ+PpnP/tZ+9rXvuaXevrTn24IfZ/0JYX/ml0Ldsks8lfgHaLEVFEyedDxGZTFoY078jNZWRiKJloVQWGsuTf0xHrPlYo5J5Noswalsm+Prq65SEA+OvwxECgzzYZdvbjgxb3QdxtKisgMCRMFSq48ePBPtOJCz+yHV9fs/lMrBuUVIUFUaOo2P1W33XMtHzta/0ARRNsZkPdrlma9x+1ffHM8YSTs/s0zr2IIfzwEKTy4X3nOtUmxxUkoVhk7wgbjxhBgFPn7zEmlF499luHBQYgT+ryj8j2NG0fba64c+fOBshk9naXASlkdsqRmlttqz42+9Vmhs5QrG56q3BiOa4Mg/ObXvjkWyQRMws496HCN+HjocQZRx6bz8BpD3U+sdzzUfWEKkRXINW84IUdUxrRHEbCNFZW2KqzTyUhaGBx57P1pHlUGlJFUgJhnVR/p+HKydouUMIcUGgsqJfvP1BaFFvAKf2XtkvF6m7U/++BY2Am3f/aa0wl6ejZZXnmuaObK5Ea554mgZ4pswkw86jwFTVKM4/bTmghvUfKSxwWSYjuSi54rDd5OJ4rEoZrwpAT98u27vAihK54xr71LQVaADeupIr0yRlJpyj0iCpUehYVtyEJeKew3iDTkEuYVZBTuG54Sj05R0cfwBiNNA9FGkrsKcWddiLp7c2G0U1E4KWQaSx6Wn+fPQ7m79dHxPCOadwhxR6V2EXQn/h4GqhZhIL8RNltrGNqKISwZBF0esOF27Bo/bwAAIABJREFUXEHoUpcPaolsSUm1kcoiZ4ivsOgm4ThEVwkZWDzKKwqY4njIfuAKwx5CwyV5YfhwD3rUtABB34VweKTSDBBd1bPDp9aj3V0kZw8gl7r2x3ffPtGa3bt3r48hERh50LAPBjFn/3blpFcas/dyb9IggWcJjzXTyrCfxOLMPXGjlZyHJmiEtEfIejI+jhijPLorD1vWHhB5y+7lb9Q8em65zbou7kH3YXFMkssItwdBv+/+yQxD7/3fzWans0gcFQbLvNQbiSaR3HzfOm2tZtEBG4q3zEuey9izicKcqI+S8mS8zE6QdIfMGCqyvLpq9sOb8KBPX6FaDVUIeg8V2EWyR+od+NOLz1g8MDzX8LAj1SKKuqHVGgrAobe6231EnvHdzLgD8UDDIG9YRU29PR5y7dvsLiIjk/ZkyAv8S1Xc9XdexC6iKlJ4fGbt8PnbMTvx6HjRB5J1VyyisxFnsCKtJEcrA+xw4bhh7zkjSekw4ZrQfiFZTnmme2etoVVUQDXzYsnQH+BoQrg56iqxWxEL2Crygo4bEPRttuQedBJ0kHnoi9ovWGSUD4NtfeXwwk88gIqgKwrrLx44MpasO0/1oBw2IQKFoE8I3JPxtUceecR+8Ad/0G655RZDyzW8jh07Zi960YvsD/7gD2z37t1jD0PC6Gm7F+2SORB0LNyq2Bizz2ldUyimqoxLQKiXuWqKQ2EAQV/udO2xtY4LCHjERV/Wuj0n0hACCNOGsAIhh0J69RIJuntEVegiy2VL3tFw60WQn1f1hacV3vMHTq26xREKMJRuEfS921B4CGHZ8PibHV9nXvPVi9MGBeIvvzmZB/1fP+NKrxROejj80iZLAR2EOw99C0WGBfXYH9eLw4Xn3PdGr5LPyuIQxqhUD8zQmg7nPNmhIqmq0CCxUvpJO0O5TXocfxGJYq4ew9tTobNkMo6wSf/C6dZjjOe3b7uwBB1F8oDH0TaJ+akujTqYE1DAkRMKhZst1og74c3wD0UHlYtHX+SMlcIPJEjOKiKmc6loXHWO6jiFsKG1E7AZeCPZylxAwl4ZDfK8tM0SdHjQ74UHPaq4V5t8NlI976QAxGdZkaUcm0TQN9BmlSlwRg/RCMhSjGWfkJcl/1kZsoZz188W4q4icZvxoB/afomvpzzEHbJGFdtxK6laf0Z08OQ9xcfTd0jgsV7cqBlKvvNVJ5eVx1zGRfd0eLHHrv9UDrHmnorMCSMYrrCmZXjzNR2F4nBdj7hBEcNIyVDYPaM3KmVLRJ1zkX3QvzYpQb/0YGo9hL3CDZBB0FXYjL28BzaowewJQwKirKKLgCvuJN5DxHCEoEMe0a9DLBXxIAXYe/960ciIXvF2eIiIqgrlAQ/2BkaP4JqTYK383IM+40Xi4EGv2ZTVbNZA0Pt233I7CDpNCnjqnV53Yg/6nj17bAqh+dla8XXixhnWbfG5oFZrWYG3qcih1/zyeiVqi5ZVaObnvMDZSKnmeIhNSsPYZ2WUVW90GT9yAx4OhzGh2aw5KQdBR94+6g2Q2Of7BqMQQNAPT9gH/b1vJ0EfkhlD++jpqs8oMT6jcpRHCowelGM5msZzDm0LmG40hjM9F8lW5XRrf15bNfuhfzMeycTQpNvNXMXog0R84QCI/HGX61kEhyZO1GV1o4vXoYx/8LOAoDtRnyJBhyz2tRwEXEXinNjj3EGikVOPOYaiphgLZIJ3CFivvusYqAp/EHJUkBdZzw0usg264XaDTd7nYdfsxMPjYZeKOi4spu4emvv5z1CJUl2JirRXxZSdHEd7TRJ0GmS1jhl5pPVKjzdC2LE3gWQDjxX3nEdou+8brA3liTwhG/H3rpk5L74MHUkEnUWaScwhK9WGTbJXbWVzgo5rMm2lbx+7//FC0M+xzp/MjwtBfzLRHvNaL3vZy+yuu+6ym266KbVau/XWW+1Vr3qVXXPNNfa+971vzDNWQvwf7F+yPXPTroAy768iO7lVEELBiWTWekVKQWQwU5UZ1O2x1XW76+Qp36RAYCEgFpot96w/uLLiXl/1ioY3FArpVYvzTtB3TLfCm0/NQV6F3B/J7Y/vHGmjvVrHHllt2wNolupkjcIJIYELUw3btzDtlkbknkNpXO4gp9ns0Dao5gP71OHxhJEE+euedoVfQwqOxO2wQsPtNymlYVGVYs8idxmB9+q6fAbYxLyK+9/meyOc/eha2/FYnEZLDRIBQJHyZ0eI9JCSgF72o96NzLLr4aGx83GIGXGVZyTzpkNxfvft4/fKTB70F7/QZlABSS9sqjHgvPAQyVAMTYqP8gEzYq4wYy9uJG+E8lT5BJIy6spDEDAZNEZzkt2Tmq2qCF5P81G5n0CVhJyqvD/XuA959RNpDfxXul171Z9O5kFHFXcRdFVxl3KVFMAz5ZTnxH1EYmR2maFPRNDPJGBGFWfXOarFmqz96TGPKMUbkfXc6657y6u4T1okDgRdReLota7aOGrIykGvolA4cnpEEGLM/G+ktsAgxkgXyhtPdwnPr4ebu7wkMfc83Si+qQKYTnKD6ON69Ir3vd0YOwTQaw9DYyKkAQiel86TPFCZoYkYUq4kDPt9u+2xyTyZe/Yd8vQkYIbcbPxUYT3lnqvbB3s70Vjr+ejxzLF3oB+6K9BDnhwaD5ieM9yuUwTd03u8+BsjrDznHLIvileCiGMcOA9wASEGZurrrPmHY/AsUSTOw9zrZjtQXLJWs4V63VY6PXvg5Jqfg2H7A69vAeX4P98+XlHMFH2we/dI26YoNhkedJdvUdFdld2175G8R9isothijsGbLg+di+4sOkrL0EW5bl5yKeRmFVlDWe9YwQDkld0HYRSq0nroXQO2dZuZrrt3bwW97jt9O7mGdnBB6uLiMkAM+n275/Bk8+69v8gK/0MGwnNZIOKmNzIqyiDhp8jaeJ1JvlUYjQrM6u9zDSdd60wXkciUfI6BO0F/y3gkE5fQvJt/mtm2bTCoVBENvQhx973N189wHRMQcXjI4fUGkVZeOPaa1hRJ9sxMeMDXg2h3srSIKDroe2yUXO+gPVt0XsBlEVkDWeAe8uhZ7mku62Z+bLRaSwRdYfYYsqwfeRV3rJ8o7qdn4R70Cfug759fSKlvck3UVHhxJMosN4LStBjiNo88jN+lYyhlCdhTjgZB73WYWsOY90TI5TkfNi5D1nFdoN0aCiyr9SsNt32PwoKhEvsUjuF15UlnlFcv86DnBP3mMdM+zzK1y0cXAIFC0C8AiE/UKZaWluzDH/6wfdd3fdfQJf76r//aXvziF7s3fdyXhPh37d/uBB1eEeV5J2Ie3kXlJ4s4sVgbWRKpCQtj9eAx6decLH/j+LIPCUoSSPpCq+WKLTzdCh+GwIJXFKE51y4teijzUisIemzJytVTyCO9RBVBR+7542vrDHFfXiENA6nDdZt1W5pu2qHFWSfoy+jL26vZychBP7QNCmffPjahB/3Hr7vcFS+F3+fPQIJa5g6FuJPIRTGWyDdX0Sh5l9gflR4JFuBBiCVD3HHrKuonCqlc0NySmytmyYKePOaxMZwPQU/+9njWobFgs9gMQf/9FyPEnekP/soIOotMyajh9mLfmRkiqOOVS1Whrv7RqedvljOfzE6xwZMGKLyYimmy+3itgNG4iGEPOg0Y4T0Prx/D3GVooKnL14cYe1xzMwTdPehHzFbgQc+quOcum7ABRWRANiszTfI0xXMjLxEXd2gc8XNE69WfiQeOvjF6fB7KrqGd4T0HM8YAL8zacTNEAU4a4n5w+y7m7aYicbXUTk+3WYU0MqxYa3s0vBykGZE/UmpIpKO7QgptjzaJ3Qhtj1aRJM0gnNUcxHVZuZwpK3lIJD3pfE+kfiNSwJzDauVj/qkXO64F8jppkbi9IuheMDI8RdFaUzKaUQaMk1XKk9cSCc9qTtDzGiOuUEZMtZP+VCgp1mFE1uAD7FMspMcUExoySNw9uiCMzApxl93Rp1qNVfbxnQY8ejbw1o3bg6CjHzqKXz4UHnSsZtzPbIPz5D/dNl5bSe2xuy65JM0NPR2lQMHA63NOynoW6s6oCRIoV749nSr2D7QL9fovG4QIj2xEMi6muPPTPucbMnao9okbXmJcnD80OLXQHWWq7jVovFBsZ2AnV1l130ldhD3jHqcRDj3o2x1jFp0SdiDo3g01kyNnJM26r7PUzpBI8S1FhO8cytNpay2Xo+eheCURupHFYIMxx1S11bXNEfSFbzNDV14ngjEI9167gYyv0wxYTRJ6PyZrv4f3nKS3zGZnwnkAgg5PehDwoa0C5D+89yD7rsuE5xyFI3FutD+EUU8GPBgkQNLBGf0fiHqX9WXwz73uMXDcj/Qo7REyhLh07ZgtPziecUNz7rKMoGePZyg6Kb0vo8rINpsM3mn7rGpLiNTL8QDyjzWyFgRdkTCqVaJoqdE2utKTQMBR0Z2GPO1Z3EdQ32S22bL5qamIlIyOI9qjIhkwGTbCg/6RQtDPY2U/eYcUgv7kYT32lRYWFuwv//Iv7du//duHvvv5z3/eXvjCF7rFdNyXhNFz9u+w3bMzEa7JDd8VmPDckLgkO2J2maik7p4G5qGjvzNy+B5YWbNbjx53zQG55lAs4B2HknOsvebKGIQrPBm7ZqZsvtWyb71kly1OtWzOQxhZSMitsJGXJyFFBU4E3ZzwP7Sy6p75e08uJ68VKt7OtVq2e3bartu15F7qE7D2/217sKPtnlcFv3Jx1neHP7p9vJ6Pwu4VTz0UBJ1kjqSSu7A8cqmPe+SgK+dc1dpZjI9qgzaqBHJ4nuQNc5uIb1YqDKWrDJPLKvKBYpdKfRXaDhohAkDiwX88KEwuscP48/cX3xcB6Pb69u4xccNZkuJ1/fM9ckJ44TM2jmLgOhHhfyLeHEdSsVJ1Yimp8qInVKIl2kZkhvVueN/8/nBoZmozlIVTOgJ5ZEFUbMec1O7tzzXUH49A9elQMV38ttrt2iv/9ENjhZClHPRXmd39qNlKmwqOP54IlXPuqec8qjjkSmX2SNPbIyAN/bkRgLnAyaoR53N3I5k06nH3Jyqby2jIaWL9VATXTjJv8c6/mVDxWtoZTQblWebaw2VgRKzmevWcIbMge6RUySvu1chDTpHUZznEYdXwWJ7kOY+IlzSvq2clKwgJej/lPboczKJi6Klm3jreV/iiwhIxN1V4DkQU50L0jUL6kcJ099HHJpp3+0DQ0S88CjIyZDM8r1G0zb3YHnLO8HMaQ+gtc7nXB8kLP1MUAZWBAxhLzuCsKpSkZ6JPSTaU9x7kw0jYMR7WJgmyGTmwyTgXhew8nScYyJSZbasNvODk7jAio/Wnt8JzQ2/NFqeabtz4rVsnI+jbd+yyFphIRgj1XL1tZZBwTzdyw0N42OU9r4Po1lPEEe5PBdqqNcbF4uIgZLbEefLGOyOo5raMb5Rr1V6ryIz8PSdEEd2FlIGpltI5WCjuBAh6n631sL93usRuaa7uWP/NVw9PNO9uemt40Dcg6Gci6pL/+jkkhyQXRwj6meSg7MEpf1zt1bTlZrJLDyCJ11wGq2XYRkIx3sv5O766smL2Q28bT9ble+zi8+j19ucYW+fqMqvDu2dctTiy/cDbEepf7C+cB8xPRx45CLr3Ju+gAKTZ2gonHj7DdxHxgPD2qRkZl0j4jxwxa69j/6M8uGSn2dwMST/mPBr6IO++s0ZDrP/r8Hy4Jsg7PpePxmv0BPlP3v4ItwfB7z4yHnYpxH1xPkUbSq+TBpws1qOG55go+VY5ND+zPbKaH5wUnhbkBB2lgvNKHTzSxxBtO1Ubgms9/gs9JzTQZJgHQYcXHeHt8KA36g1Dp5AspiapfPm4PcT9vvHqMp1lWpePLgAChaBfABCfqFN8//d/v3vJEcq+f/9+v8z9999vP/zDP2w7duywP/7jPx770hJGLzq4x/Zumx3q++g5hqEkDRF0MaHwDCqUxlWyWt1Odbp2vN3xYm1fPAKvPkMWoWzBU46Ff2KdedOQOBAge2dnnZh/2+5dtjA15cRZIdtSHPCTYeDMv8mtkw+dWkkE/fDJU6lwEnJx5qeatntuxp7mBB290pnTfLTd9etcHgT9v3ztjomUh5dfezBV5/V7ynJY5XnzqIMIdyUp198MUVUfdDr+h5mQ96v1D3KKh02Q11L35dh70xwgGQ9v22kEnYqs14KJENqKoCt+qzLKKC9RQp3+LXqqbtokQWeuVbXTiaDLMpyUc0VsZGS5umdRefnWtU0RAw8Xi1eOLowUJBt8l5EZ1daVlLzMG8M7z/tiU2vB3Ndd0Kof2zq+4DmZmQYEgt7p2Cv/7M8nmnM//CNmdz5odmqN3gePhuPA0uCHrPcbkGst47ScN5AeZ1RYzyBp0lPMFZcRJUZfzbEKDSRFTOjvocvAe9Iza0O5XDf7xmcnU7xQJI5FcpRrXs0+ea0VAq/njDBeeC6qcfI7Hiod+euMYOFLc6S6V3qxfZ1H6zAdOwrPOsKzQVRz4psB4es1RTUxBx5rSHUssKZVj8Lz5T2vEUofjVEgr9+ckKDvD4KO4fi8CwxczjhpYSi+1ysJYwXlGVKjSNKxrknQqzyQRNAjB0jmN3VISJXyAwcRdHmflP8OA4s6OaTohORlC6QjzcePiyqcIOhzRoK+q9X0NITja8xBx7PAc0bEEuTde26/daI1u7hjZ0pRkKAIrpxa1Sm0XcXv1G4N+d74h2r0c9MwaFZEGbnfXiAwQq9ceQ/DiSv+MSHZox6eUebAhg02FHnJv8w+O2QGlYFXxaXoYWVePz2xIOOn2j0nS6zqjvf6JOizLD77qS9eOIKe5PRGUT+ZcXxDgp7Rkyr1KKMso/IyE1ZJbm4gU2VkzElZLofza51BhGY7IY/YLEFfeF54sVWQbGC2vmLW61CeKv1laF8MMu7PODzvyXaPSI6G2TSq6mM9g2iDoK8RP3jYsT7xuRP0aRJvEHfgc+Ik5oWZO9wRubLEcHlP1aqbnTpl1m6b9db5D1Xs8Q8ld0DQ8V18Lq+/h8XjXjDfIsxeeezwvK/eN9k+cdX2xSTP+fgjby6cB6PPj/LsDJtddnDuvKGMAm74yf2h3e34z4rAx7YT5N7Td5gcSt0liosyt73ynEtv1h4Fgo6uSZCpCHnPXBHJcSODKKOu+nbLmCkpZ5rT5f0Lg0Ah6BcGxyfkLIcPH7aXvvSl9tWvftUOHjzo18B7z3jGM+wDH/iAHThwYOzriqC/6mnX2lXbl7x1lwSIexddSkTWcuzopB2VABEJlffoaLttD59asTtPnLRPPfxIbABUZL3dmnt0Oql1GkKcr92+ZDump+3pu3Z6GHwlnPibVGjmBI+66gb20KlVe3hlxR5ZXbUHT4UpF+1pGg1vH3fJ7Ix9y67tHg54dK3jBP1Yu+PK5KVzs648/P6tk7XP+R+uuixawlVkW5hAKaU3VS1xqpAseWdJ5CTbES49TMTpc1GYdPU7leCcTA5vDvLypiiEeJ58ruE9D+9x+tuV2VzjEWGN5xDGA5Jmhnz+/tcnz0F/z/UvcK9Q6OV+pwwrJ2bc9Pg/pQdIOa2s2pnnO/M0SjHLK2QrgoA6a81bQmmjcx4d10xUKwivkK2q2ofX0Il6hY2+DwidZLiCrFrGwxodir+84s8ma7P2L19v9uU7zE4syzsfSz+7xOgGn6AcPXTEIEFsQinYUAvJ3hxRUjfQWVM2wplONarSnE3Hce9deGy+NCFBf9quS02FCEWWIQc0pzHO9X4vPOZEA15ohvwytxlKkVrs4bsybjlR6rF3t4x1Wlsgj3gf55a3W8XiUncgTkKueHUSiJY6wk9t18Kc5OR8rgW5zWvielDAILVB3pX/LtIL+Xv3sck86Af3HbRaoxk585QhCHUnaSEBRIi/cryxvrS2eyDmPkYYDbiwQN6lSObrG8YG7Sc4TkY6yUZFA+mnZKEXicuiR1Tsj50z1AIsZGnM88YAxSbN0GwT+egLdaYBrHlxpoF3W+CeA6W1Zx8+fOdEBH12YYfXKsH96n4kv33+Rfg6iIcT8ugzD0yn0CauVbP5maYtzpKgpxQor56eVVLvMdLDrxUL0qMNskrxynXHYGScHFqfSl+QYTImTy4X+Mz5Aa7j+5wbC0jW/WeP6QFz0zXr9/p282fumQi7m95mNgNCuIGGs5EHXZ5+rqNzq0Vn8sLrm6l9l2SnDBxnOfWQgTQ3CJx7ONURqJmDHPS3jkcycYJUJO7ZEV2FdA6kQ2GNxnjaq/R+J6IuG1bkhePZsSsCcfS5qUitqMIOcu5EXUXiYgNJxrNIwUKhOhD22VkaDOaWzBpIW2Bt1RRyj/PgfEh8wzngUcc/FKXDPxqGqjB7kHKQevfiw0iAccZmB4J//+fHw064fceeHdFZpqrh4cYMVUHPn+NQIePs8aWuQ3IYULnxfcJboEkW+I6RisT53iGnQSw6GQC8XWcYRiFHELqOEHbo7tiTqEtGZKs6Hw1QQLNhsy22UmaND0bRsLAt5c2sy3aC1xn07UN33zfWeh1napdjx0egEPTxMXtSv4GFhzz02267za973XXX2fXXXz/xGI4fP+4V4V95HQk6CK0sqio2U6nroVRlBB3vSPFRiOfR9ro9vHLK7jpx0v7q4UeCcDFPEHkyHsYTlSph9fTc8+1LXpX8up07bB5lQkPAyrSnfo0KRVW4jxQGkPOHV1cNYYkIdafyQM/SQqtpu2Zn7Nqdi65wiaCfWEcbNrRfm3aB9oe3fd0jFJDrfz4vYfdPL99X9UZ266YKN5GUewhWyjevPLv0EFd/C+eKDFZEfajwiCuvUVVfzu7TcqWzMOw4hptBVZiIobIgqQrxVpu1UIQIYjwCebz0HhVNhLj/4V33joUbziDs3vU9z2X7o8zmIoJeGS6IyDBBz3ELnNQXOXt4IslDLawS/WQf5uDgMeUS+tVRufc8D20P0xU3OYXky3xFDagiGaG1ZGNb7XTthg9/dCzshNvrfsLsq0HQuSMPRawNRQHoyhsqt6cPq1p757MIdMyGzDw+PIuCvNFHQ+/lf1DYeA4j/n35C+xiMe56vW7n3tRCUiHVIJWetxdQoiAPQ9r5oveXOYIi6CC/IIYg3ozSqHtYb6/P9mkqeKY0EihReLX7quIeoe+RL53576q56FV7lf7CsaRIlxgbCqEhdYih9PwcIY0i6BgTrikocQ/3HD8yEXYH9l5mNkLQ5b324USxu4qgh0fHjZQg6KrOHkWKQNCzzBzVfID3lcYtMiH+rKa5creVGy3vMNqmKTdee5N3xIgopbxOiMtDb+fMYnEzSHHwUHcS9HX0H+6jiwSfF3BDJMUnHvzmRNhNzy+FB/30Qm6sL0DvtocWg0xnBH26VXeSPj/TqAh69JaC7ERhweVVzFkUIawIuiawFyFUj/Vo1ybcFfoek66KwBnKZR2ZnYk0hLEjOhe44SDy4hl6jFQNs7kWMb3ls4cnwu4333R6DnoSPRvIni1F0GUkCXvG2UTlqMgFnoiS+rFfGU/W5Xvs1HdWBUtR2NFrNcRPEXSQbM8uyQg6fhdJVr66rxeIMY+EIZF2go7fg6Dr/tIalVGgRWI9v0CiPb/drDGtaELKWNc9I+weY4T3/iQI+kmSehB6FLAD2cc/kH203ARBd8/6Kgl6NGxwD/zhz4+HnfbYb9+9I/YEGfZifKMEPS9Sm+seMrQm54zcTFGYLdJ5aKw7H4JekXscTV2u7m11RdAh95NRMvYqyi5WhYczjPtRRtDDUOoEvdlKBB2y78P33D/Weh1HZSjHjo9AIejjY/aEf+MjH/mIve51r7NPf/rTtri4OHQ9CJPnPve5duONN9rzn//8scdy3333JW/82F/+e/gFRCScbyRCwa6aAOPghm8V7CbDruA2LHTGmXcFu4LdhdqyyrybHMmC3WTYjYNb2WOLrJtslp3+rXHn3YW6bjnP6QgUgr4FZwXC2tHr/A1veMOGo3vnO99pH/3oRyfKQUd+3QMPPGAoQFflGW9BEJ7gIcGzcvLkSc/tr3tf33O/CnYMpRoXNyBbsJsMu4Ib1+Uk865gV7A7t1Q/+xFl3k2OYMFuMuwmwa3ssUXWTTbbqm9NOu82e93y/TMjUAj6Fpwdl19+uX3wgx9Mvc9Hh4hwd7RZu/fee7fg6MuQCgIFgYJAQaAgUBAoCBQECgIFgYJAQWASBApBnwS1J/g7MzMz9pWvfMWuueaaDa90xx132DOf+Uxb9d4T5VUQKAgUBAoCBYGCQEGgIFAQKAgUBAoCfx8QKAR9Cz7Fq6++2t7xjnfYD/zAD2w4uj/6oz+yN77xjXbXXXdtwdGXIRUECgIFgYJAQaAgUBAoCBQECgIFgYLAJAgUgj4Jak/wd17/+tfbLbfcYp/5zGcM3vT8Ba/5s5/9bM9RRy56eRUECgIFgYJAQaAgUBAoCBQECgIFgYLA3w8ECkHfgs/x4Ycftu/8zu+0RqPh1dyf+tSn+iiRe/4bv/Eb1uv17HOf+5zt3bt3C46+DKkgUBAoCBQECgIFgYJAQaAgUBAoCBQEJkGgEPRJUHsSvnPPPffYT/7kT9qHPvQhr2CMF6quv+QlL3GSfuWVVz4JoyiXKAgUBAoCBYGCQEGgIFAQKAgUBAoCBYEnC4FC0J8spCe8ztGjRw1F4UDSr732WtuxY8eEZypfKwgUBAoCBYGCQEGgIFAQKAgUBAoCBYGtjEAh6Fv56ZSxFQQKAgWBgkBBoCBQECgIFAQKAgWBgsBFg0Ah6BfNoy43WhAoCBQECgIFgYJAQaAgUBAoCBQECgJbGYFC0Lfy0yljKwgUBAoCBYGCQEGgIFAQKAgUBAoCBYGLBoFC0C+aR11utCBQECgIFAQKAgWBgkBBoCBQECgIFAS2MgKFoG/lp1PGVhAoCBQECgIFgYJAQaAgUBAoCBQECgIXDQKFoF80j7qj6BW3AAAgAElEQVTcaEGgIFAQKAgUBAoCBYGCQEGgIFAQKAhsZQQKQd/KT6eMrSBQECgIFAQKAgWBgkBBoCBQECgIFAQuGgQKQb9oHnW50YJAQaAgUBAoCBQECgIFgYJAQaAgUBDYyggUgr6Vn04ZW0GgIFAQKAgUBAoCBYGCQEGgIFAQKAhcNAgUgn7RPOpyowWBgkBBoCBQECgIFAQKAgWBgkBBoCCwlREoBH0rP50ytoJAQaAgUBAoCBQECgIFgYJAQaAgUBC4aBAoBP2iedTlRgsCBYGCQEGgIFAQKAgUBAoCBYGCQEFgKyNQCPpWfjplbAWBgkBBoCBQECgIFAQKAgWBgkBBoCBw0SBQCPpF86jLjRYECgIFgYJAQaAgUBAoCBQECgIFgYLAVkagEPSt/HTK2AoCBYGCQEGgIFAQKAgUBAoCBYGCQEHgokGgEPSL5lGXGy0IFAQKAgWBgkBBoCBQECgIFAQKAgWBrYxAIehb+emUsRUECgIFgYJAQaAgUBAoCBQECgIFgYLARYNAIegXzaMuN1oQKAgUBAoCBYGCQEGgIFAQKAgUBAoCWxmBQtC38tMpYysIFAQKAgWBgkBBoCBQECgIFAQKAgWBiwaBQtAvmkddbrQgUBAoCBQECgIFgYJAQaAgUBAoCBQEtjIChaBv5adTxlYQKAgUBAoCBYGCQEGgIFAQKAgUBAoCFw0ChaBfNI+63GhBoCBQECgIFAQKAgWBgkBBoCBQECgIbGUECkHfyk+njK0gUBAoCBQECgIFgYJAQaAgUBAoCBQELhoECkG/aB51udGCQEGgIFAQKAgUBAoCBYGCQEGgIFAQ2MoIFIK+lZ9OGVtBoCBQECgIFAQKAgWBgkBBoCBQECgIXDQIFIJ+0TzqcqMFgYJAQaAgUBAoCBQECgIFgYJAQaAgsJURKAR9Kz+dMraCQEGgIFAQKAgUBAoCBYGCQEGgIFAQuGgQKAT9onnU5UYLAgWBgkBBoCBQECgIFAQKAgWBgkBBYCsjUAj6Vn46ZWwFgYJAQaAgUBAoCBQECgIFgYJAQaAgcNEgUAj6RfOoy40WBAoCBYGCQEGgIFAQKAgUBAoCBYGCwFZGoBD0Lfp0Op2Ofe/3fq/deOONdu21127RUZZhFQQKAgWBgkBBoCBQECgIFAQKAgWBgsCFQqAQ9AuF5BNwnt27d9snP/nJQtCfAGzLKQsCBYGCQEGgIFAQKAgUBAoCBYGCwFZDoBD0rfZEsvG84Q1vsOnpafuVX/mVLTzKMrSCQEGgIFAQKAgUBAoCBYGCQEGgIFAQuBAIFIJ+IVB8gs7x+te/3m666Sb3oD/rWc+ybdu2DV3p13/918e+cr/ftwceeMAWFhasVquN/f2/L18YDAZ28uRJ279/v9Xr9fO6rYKd2SS4AdyC3WTYFdy4NCeZdwW7gt15CfazHFTm3eQIFuwmw24S3MoeW2TdZLOt+tak826z1y3fPzMChaBv4dnxohe96IyjA7n+yEc+Mvbo77vvPjt48ODY3/v7+oXDhw/bgQMHzuv2CnYVTOPghm8V7CbDruA2vDTHmXcFu4LdeQn28ziozLvzAOkMhxTsJsNuHNzKHltk3WSz7PRvjTvvLtR1y3lOR6AQ9ItsVhw/fty2b99uv3X99Tbbap3X3cvPPjivoyc7qBYn7w/4S88Ght8aVrO6e/r5t8Vg+OP0CIA+vLXxiX86oPet0+/bcqfn35ht1q3d7drrb/moHTt2zJaWls5r0MLuXdf/dzbXnB66fhqJ38jABgPcQd8GA4yG75nfG//GmPj+6S/483G3PKa6y1pNnv5zRD4EmH1dF+fAuQY8V3/Qt36f49E1hoZSM4+uqFvNGvVadhaztb7ZDbd8aSzccA/C7vXPepY1Gg3D5fuD6tyORi2QwrgwBwaOIBFLUNUM06FCYOC/45+PN+Ac1Go2GNSsEyjzyedY8m+/EnCJaJKGzj2I88bf+NjnIX4axs2x48s4U7NmNuX/ajbbaPg4WnVeg8/BbLXbs7d9/BNjYSfcfu0Hn2rtfs26g4HhtOcT+5IfU/2u+z6vKb/pg2LVBvanjzr/fHRF45njXpt1s7VOz97yX++YCLuXP/07rTESJaPrVvOKt8pHXM0Vn5cxH/G75louhzB3fP5lEUl672wA+lUw5zPZp991rfxB5+hp7un4FA3F6Tz0Wu/17A+++rmJsNux+2pb72Ot8h9mey5XcbF6nbIivbL5Wd3PwNcY8RPCFZ54zsBvqh5YusziNYfXLd/AsX5FX2PxvGoDa9Xr1qxjnWAPqcbdrNdtut7gfKrVrVE3m240bKpR932wUatZExMtnref2gbW6XXtv335kxNh97LZV9hMbSbJpuFVW42b1wKCkm3CUvOwQq36ZBhFTr2NpYLmg2Sd5o6eV+xY6fGNfk4pl/8HXPV39RllHaQ1ZV5nsG7/9+rvToTdv/sFs+mpoSl1ht1yZLJDlp95ax25R673hNzwFD7b0k17la/fHMizfmv4w2rfqsaAc62umf30220s3HAG7RW/+KwfsGa95WunGxoHnxf+JjhYGy7X4p6b0LP8H3SuWKPCIxMo+TTzt+N/vl/HHME1KTc5F7Cn+s9Y95zpWvu85rBsg14SsjGTufxOknzxHf4tObHSW7df/uIHx8JOuL3g0BVJhvN8A2vU6j5HBEG337eeLpZpcNTX9Bwx0sC737d2D2hjjZu1GnXb1mqleYfvdPvajQKFbL/xUcSJh/fSpFWetuqpu3Ezoy7J9ao9ar7VsEPz89Zs1G26QcmNc2OcN35hfN1ujClfDh0TgULQxwTs7+pweIPwOl9v75nGeeLECSekv/d932dz50nQn8x7hvCDQFoPcgalqgWyJXYZg6lUvOHR5XuzCx4oagMIn749trbuwmi+1bR2r2P/880f9k1tcXHxvG5R2L33+15k25qhPWiH10ZVEwEnQXeDQyLjFJLV38M7U9qk4vhhgg5DBbcykM+zv7gxYhvGGMjXB/HTbNAfOEnHZRAGHLvpMEeHAhxEHb+IuICgv+LDnx8LN1xd2P38P3yONRstV+N6uD6UhoAomTLi9kBGnaT7PxlrqDxA0c6NGLgZjBFKgB/hc6ZGRUTberbRitBo45VhyA0ZoVRwQwtFAptd/O4KRtpLqajiyYCkT9frttRsusIPsuEbcCita72evfEjHxsLO+H2U//0KfbwSs3WugMnIEOKpSZDUjar+TFszOCBUsrOOofOOcdGvx1IDv/g9UYVvPiqFDwZMFynqNiEz8dabWBTtZ51u337o4/fPRF2r/62Z1uzDrWzGoiuXV2Ov+UkCSi6gcgNWfy2z64MZ+E7ROzPQpZy1Ib0X18LlZLHsZyRc6XT6BzV4+I7yfBmZuu9rv3nL35mIuwu2fuURNDT84r1qucKUoz5TgKv9RpUPpH1UMq1nmBMC4OWE/d0I5oc2fPIlPlkEGvUfT26TAwDLj6bqtfdMNaBUXZg1ukPXL5ALjgJxz+Q9Ubd5lpNm2o0/KcrtfVQZiETg6B3e137ky9/YiLsXjX32oygy4SR3agbGSivKtLC96pPZPyg/MmpzLCZg7idbWegpKpeFdUJuZDMjfqE+wafM7+ttSrjr86pM1OO8t31Qdt+d+U/TITdjW8zm5nO1lxmwD3HTVbMZUREDWGTyFTgNgJckpFDizTDTnvJubbiswpZGZaHnxsI+g2/QMJ9vroJLqO94t8++59ZvdHw/RXrALMZ+hT2QuynTtiSw4M3AILeDHLuRDoM0acPP9MjElnmUZobpKOSQdwb8czc2ZLNUc15/dT8TWQ3MK4eAWegjvOVo/0mDlrtrdubP/v/jIWdcHvBoStdHjghj/OCkOOqvtZcoRxGxHWTWCMyKugIHA7H0FoXJgtiCoPgwlSL6z2uAxmF00pCSBeRUUXno4EtDJ0xFn4zR0j6UTUxfUW6YZ8yZL7Vssu2zboxc6pBKYJrgaD/xy98aSzszjG9y8ebRKAQ9E0C+ER+HeTpl37pl+wd73iHLS8v+6WQO/4zP/Mz9nM/93PnnTudj3GrE3RYE11YwFI5GNhco+GCxCVjthmei6AnkQWCNBjYWq9vD6+SoC+0Gq60/ouPXECCHsJzEAS97x50GhvkLZf1VyQw7TTxgIZEangofDMKGVwP/zAJeq4ZjOwaYSU/jaD7rhmW88C58qSTqPuZRMyxaYTnEV4yKLFrvc0R9Dc/97ut0WjSmg/PHDaxEYIuou4E3RV+jhnKdVIe0iZKQ4M2LlmOFW3QD3IeppGKnI4oXlRcqHzgOflmJoIeXtVE0JMnsCJw8jzMNCqCDqLgBqKMoP/MhAT9Vddfa4eXa7bapUc5eRA1d0YVxcyLKc9lmiW512cDATY2N5dCVmkSVNBG/k6XSh4BvkOVq/oC1gfVfDyDgTUHXev1enbLZ+8ZS3mQrHtNEPThVZKRlczrLA4kok5yHuORUp972X2tZF7h7LNz7Q2bJejD5HxYIpJTcdybI+jXWqdP8q1H5DY9kXQzJ8WIUBBO7mVKEShVPIJLrczIBcI8E8YmGtQq4pA8PsGUuPaolbqiW6/Tc+8eLmnOJOiQEyAkkB/42enTewUFGJ/BiwVFHIbaVl0EnS4u3AOMl3jhO51ex/78K5+aaN69Ogg6Rk65lcvtnILnBD086TIWpWiBPK4jxQwM7Rzn4oqa8dU6qNYA1xuF//Bx8VfymFNOjhLz6m/RtIGt99v22yu/MRF2IuixHXHuxQ1uaGCspgBVhdEtcXQxDqsTlYFIoigtrlHhFutAY8kHeK4FP/K5ZsPocwNB//FNEPRf24Cgr4WxmwZr0VxNH5HziqTTCM6RVbxUUXijklSyQXJbcylmFITFuQj6EPkd9qBXY3CXR1qb/r5YcYAID/pbJiTozz90hU03YKaoZF2n33NZJ9k1+oilmzDKr8JFey4I+mq3G9+H0T4IeuhY+Mr5EfRqVSZynw8m9jAZE0brS2FseA9G0flm0/bPz7khE/9I0M3ava795he/PNZ6HXPKl8PHRKAQ9DEBezIP/9mf/Vn7nd/5HXvrW99qz3ve8/zSH//4x+0tb3mLvfa1r7W3v/3tYw8nJ+ijIe4jnGVDQ3Vur9vo4uf6fIN9Mm0C+GWt23PF6sR6x9b7A9sx1bK5ZiOUsfzbp1N0isdMSIaXdq0/sJVu3+5fIUHfOd10pfWNH52MoL/ne7/HtrWG4+9EwkkptQVW3gcNa5RakxBU3qWh0PcUH5oxg42eymlhraFu1TzIM2wb2iwF0cBDtXp9hLv3rd8Ln4jvRpX3HAQdG3XDQ6FqttIb2Cs34UF/23O/22ph3W/j+mljkbfb3JgCyz8JOkLk6KHD5uKheB6mqlBYkSeFupMB4FMpnHJFEpVKKc0VI6mVtPDz20mBChIm0k7yxk8Zgs9j4aPFuGYjlLYKt+e8hSX95z72F2NtgFqvL/tHT7F7lxu22h1YqyFvGs8r6366n/hFcyumWKV4DEUS6O2R9XQubf8MkqcKx6sOIFnMtJ54DjII5R52RXPwxmo26Pet225bt9Ozr3zt3omwgwe91WgMez9yjFKIpAaZr0dFapzmPElkU8qQPJyj0I1yhTN+PrKOh+Xx8FlyL371fPUMM+IFktrr2n/6wmQe9F27r/X4EHpvOCKt2ciScY+0r08nzDVbx/qF5ykMBBXXqdJTIE2gHG5zT3jNmp72MrDVfs/JPaJNMC9wXvyHta+UG+A802j4del9pyHH16B71ekxJDknQQ/7qY8R10V4+7ZW088x02zQYIjUH8idPn2AUNV7/a791Tc+O9G8+9G5H7fp2oyfy0NlFd2jNZsF+zO8OCftTLuBDKk8jMMkfWN/+ZlM11qPIlE5Ta+czvksq+RlRseT8UyfSqbm5+Xx8KD/5qnJCPq73m42Q+gqsp0tnCE+J9GSy5hzEPSwwUiADkmz3EA5FCCXy7GcoGdRNZTHI6JGsmZUwdL34n0ZKldXzW540+Qe9F/6rv/Jmo1mik7DedcGMJoxxN2NjtlmkeaZR5rQMJUTdAVB6960s1Z/j8j6BKCsepwrCp2Xsc3n/AgxT06CbJ/OZ6qIsox1lDGVYWC117G3fvYDE63X7z5wyKY80oovNxiGgZa3lEvuStsc2r/8mVbHdXp9W+sFQYdMqSNyB176agLBiZB75l0XiWik4W2W3xF+GoGjS7UtDfx0Y1wYKWs1l3v0oMNYKZkDgt6zdxWCPgz53/FfhaD/HT+As10eFcZvvPFGe+lLXzp02Pvf/377qZ/6Kbv//vvHHv1pBD0lzlQbOxVqWtzyl94Xodzo4pWKe35DGz1+pdtz5e5ou+Ne9Eump9zTAeKjNFIaAWrJs5wE6oikxPAR3rjaG9ipXt8OnyJBv2QaHvSe/atbLhBBz3wKIugMDcw0BnHsLP/SRX7mrSbaeV54EO0hkE5nTgpBrDaQ0EhrEP0Rwq5nGmDBw9Ab9K3bI0HvgaCnuRBGAw8HbVitzrBQgLfS3RxB/8XnfrdZEPRV97TRU0oFmZbclV7P1kHMPK8NudzwipEEIIzcw1RDeYVCTm9elTP+/7H3JtC2nVWZ6Ld235zmnnO7JDcJaQmNQSQgIiAlxioQy/GUp0XpUBQspB6dgFRhlQpPraqnvsbCwipRaQI6LKUcw/ccPlEEUQQUAyTEkAQSktzk9vfc0+5mtY9vNmv9e9+TkL3PDfLGuCds9j377L32Wv/6//nPb85vflMvcdor0t/DunyfN0414zkoIKicQlKsfVMsHQunpkY1OR4BhdBtDaCzDl3AQnka+t3DLMXPzQnQv//bb8CDOzUMUtaxVXT78hqMgneeH+GZx3DaTNEz9U9T3u9jW767vGuSpl06V6GvZt6I08YnAXqllSA6AlmO8XAkAP2ee+YF6M9Ck5kRNRzB1VY27zx6ecBo1PPcnX6uDmMF6PkFJj1Q0iQrV07HeXoFT4/R7nY1QBzTtPHAKVR/N3RpFaD/9mfnA+irB64HImbHKwdQtCOsdIjfxLVJkM11yqw2bSsd0/OySkGNNceI66RPvQbal3pNbMEgS6UmcygAneuK1dm6LlWPQKnt3YaCax97BfDqvPLBvUNAugF0txFiQ6YAOsG61EwzUCmgXvOMLK3Kswyfue8zczn8P957NdqRapUoQJfZYfNPf/P/d3DuwT99PdTVqOpJLZ83MU0eDZafP98q930S01bQa1dwHtCWwyw6x2o36vt4LwD9PyrFffrHr2UC19mbwu02BDy6q07+TIOq8K+PCtD9YFODWgXMqiOV5mZ6K7JjuK12sJzb+0YE6D89P0D/+We+FM1Gs9pTA4CeGECvjJAak3phjDHOOVvr03NRrZeG68J9Z7q8ZnI38RI/B/7K/tK16uUbDkgr1k94z3yu6flUs19tcgVT+a+9APRvvfxKCRTK3LJgKRMYpfsV3PNq/vhaqnZRYQPZG8gmYrDRx4S2h7ZrEkxXBy73mkfcf61MwBhHzpIqM+fOkDhvgnvZgpZ4HlkwinsgaEO7/a7b7pjJ1s3tJlz84GMagYsA/TEN0z/OmzqdDm6//XY88YlPnDiBu+++G09/+tMxZKh1xp+vZ4o7DeFmkkoG9ewokedLui0sNetSM0jjpobbHZvdLt5Are1ApE9vpQW20hz3DxLZGA6162Bt4c98bD6Afotk0F1gT823uqN08MrqrvJM3QlzE+7muDL8gfswQW0P3SfbNcpofVUXO7khhlubAvXy+8xo++/cfDSDXiBNVUbNAQcpqwTm8mxZKh55J83wwx/+3MxG3Ofd27/12UCtIc44aXfMkm8nqTjHDM7ERS4BFWbQhaZKgB7Ria+hI0JPNZBGTlZFK6pETmqBd+abnDjATj8u98BqPNRBqlxb/k6wUTJmHUh6Nt1pegbQZSP2Ojl3Oqyej8cSwbkgAEOq20//5Xw16D/wwifi6A4z6AxYOFgK3HIHbSHFOqC5l5PAxkFGYYIGP7WWpr36x2hnQr9AZq8D8yo1omvYs8VWex1m2CvaYiTBo9FgKAD97nuOzjTvfM69/BufhVatoi5W1x0GJafWU1hyWNZWV4PgznX47EdzamjlOgYIYRcK/CMN9eRYVjDfndQK4AXZEztFz17z171k0HvL16DOIJ0BcHFexQHV0iFaO8/wkjpOIMq1TMdUheUUpvCcNWNWBTOazIQb4Ob6FoCeWgbdqKVKYXeAruCax2DWW0G7Ho9rQijsNX2vZM+ZkU/pIOu5eB2m6ENQHE5AvmbTPWsvNaW50uh5TM6/L9w/m+aGz7uf6L8GHQPoFTyvdoHqvrtAl7MB/D3OzDHBTgP3usO5ta7St491yU6DmnCe+u6pS9fqawPeUZmtfBTKuwOqvQD0d/4Hlldx/AEHrqUeyFQu8zzTFKLxXQZlgv4+jdz9/cHwTh9/Ilu5i12UNRK+bkh9t9Py8Q69liwBXrcHivvPPfP70G60lIxkwW+uBa1Jt8BZkHV2/0FWkgW6w0CiBsFCMVa9Eh+qShxzci+tjqvv9wCaBPSC0g3PguugVaPEf7lYm16LBsOZWEjyVNY7164HEviOQZbgF+bMoL/0STdIQoL3l4E92rC14UgAtui+BP6En2d4TylKGzIueF08XwJf/5GynkYlCKmvV+KUcp3GUpn261Tjw9hAuotOzDP13Xxv9YRFaGU0KLLYauCqxX5Vg242lBn0X//sxRr0x+jqfE3edhGgf02Geb4vefaznw0+3vGOd0wcgP3RP/3pT+NTn/rUzAf+egfo5+IUQxN0o2N1WbeF5WZDjJqrYs8C0KlAvJEV2Exz3LeTSFbzknYN6VcyNG/7q70AdDr8uhU7pV0cGlNtd9Op9UK6cbkIiHwu3IxKpZOKJui10HqDHeWYQAr/aAJrnhac9ENCYD8F0M1Z4PsVoNMhZQbdAbo6vcxoCfWN1OBgpyCY/uEP3zYTUOLHfd69bReAvj6OhV7F4Mw4z8CSBAXomlUXCq0A9LoopBOc9xsNAev83eu/lRc2SeF0B76qp5vMtLsr7FnPpmUDxYn1zGkI0IM6eLmrisAn6pCZgVAavm2/dvBBmuLffnSvAJ1K1SHFfUowzr4zpORNEGFKgK7T152O8wzJY/X2d7FADixL58XpepNxKFsCdr+CKSsA3caV83I80Az6XfMC9KdZBt2dyjAwYW5mmCGXJRK8xwM+gXivfmoqAOJkwfMA+hQImCQmPXLuMzQRIWXxqwH0yWvRDPp7Pvf3M61ZX6+txauFfcApzHptXpucF9lJtB1mmxQkazacrztA11p0na9SKiNCl1Xng7Y5vvybguRca8ctc0UtCQHoBPJCEVX71G4oo8azcATTdNW5NqjQrraDpU2ZlDe50KTT5Jnt7xLk1wjW62IHPWvvbByKPOZ5hnsenC0g6WP36v5rS5E4c+/LjLnbHV8+vDq/Fn1vBcPVvtVAaakQdJHZMGGcvxpwtZ2knEtT2ccQLFb7WgjSfTeyPU/2IOckTT8X2AtA/7VfVHAuOiUGll1gLMTOpZn6KvZq+s+PWqO+2xecV0Y2SXieNoPhDlwFKXd310pxVNs3GJV+0/86fwb9Z28ygO5GytlpovdiayGYC+YSqKcR2Cq3TD4c+qwZ9IoxEHRw8L3HbKr+qiJx4v/YfkNmShise6wAncCc/gFLUJgp5xpeaLRM/EzXwk4W4z/MCdBffuM3CECnnWfHFQYaj23vYCdJRLdC2HVBQD+8mxPjZhl0DiXtSmwlM/ydQXuW53jiIFy/brc9COY2wuduKcIppMhJcK47ud4XB/kls0vstfmgEbDUbOKqpQURiGOg0u8vfbB3XgToM2Oqx/MDFwH64zm6ezz2xz72MbzkJS/BlVdeiec85zlytE9+8pNgn8I/+ZM/wfOf//yZv+HrGaAzwnt8FGM7peK6ZtCv6rew2mqg19DMqTsojxzgDmPRwDAHziQ51pMcd29rBv2yNqmLKX7lEx+dy2m95UUvKDPoDgfLuLSLjdkOpufp6qVuag3Ymwfuki1yLM+gc7OeiI/SqZVqpXJr9ejyI7kKuj87uA8MtxxDxYBkc6SKKLNGwSZKqmqYXXZF0e00xY9+ZHYhEZ93v/jcb0VRqwnwHlsG/exoJI70icEAPH6ixHzkpJDLOOrDVZgptEJGBRX+CdYJqjk3SHunw69AwAGBU0X1Wevg6PD7hlVFxn1o5e6UUWz1zLS+VsWp2ka3FV1wObXqrqgzrcfWrk2m1sogRZzgjR+drwZdMuisQc8sWxigvAl6pTllu9E09bZX51p+LgSablF242w+mrUpp5n+o8yQ22cqgkOVqT6PJmhLt3K6IwkcEaAne8ig/+jTniU1mRX3Jlgx/mXVMpm4Srua864nGMqKnGBjVoLYEgBNDtwjYYnKIa7u0eQn3TE2tBBk4/Wz5dlOWI69ZNAb/au1zMViUaXdkKmkL5aZTXO8tT1i5ezrEtH3cQ37yYmmRDAYtGesGZe174DIAHyDtZKyrvVKZf2S6m616BSb4/qnw0kAwPaKys7JhPXDung6oAz2idK7ZdD967n38D20cwxcyvqNeB4ZHnx4tqxS2Xmh/zoB6O6EV5asqjfXIJ/XmTuMVxuvFpqlRxnaUQMdkLVlLR5Jvz8P7rtLPzlrvLY5JLZXM0VvVEhZ96Cdt1Lj3lZR2Muz0rOzz4bw3DPvcTGauwb9P74VOLMJjMZAZur65UIrR/R85shuJmoiIObicKUy5fmfEDNkN2syyFjFykPb6TtyuOjcC7EtfQL4ht/owS4pGcmBZoMitsCv/vr8AP1nbvo+dNhlprQPup8q66XKoJeAzy7L93jOKwHVU+0jS6Ad0p2Cm1KKaU7yO5RJFmgpcH2WpWQ+GDbQoW0sO2hYGuT0aAdf2j4r65hicPtaXTxl+aDUjQyjsG0AACAASURBVDM4yB8C9//tM/OpuL/1Od8sInEMMB7bGWA7TvCFtTVsjGPx99Tv0NKaaXjsc8CDtH5ZtIVeOsPx1TIdfZfYwsDWObOlmq+BrbOWt/xEVRcfdL8oW0qattAUxT20waS4X2EicUx86Tmoivtv337nTD7xo7kEF/+29xG4CND3PoaP6xGOHTuGd77znbjrrrvke5785CdL/Tnr0+f5+foG6AUeGMbYSjKcHinV/dqFNg62G1gIAbpRic53dKdSVQAGOXAiVoB+51aCWlTgSEsB+js+9ZczGSMfu/e/6AXSmkeNsn7nJHHMXDLPUIpTMG267ZPi4Bgt3h0lr6cOslMaifYWI9YiLaBC8RvDrKn8PjVBvH3HxMuOpMrvDNqrBW/0WqztJMGP/eU/zDRuPIyP3X963nORC52VGRZ1os8MR9hJExzd3sFmEiPjlTIKLXXvNevZWjkMpMhxk5cWSdbLmBl1bqA9i3Qz++Vicp5hd4Cur1fU2SqHGYLHqnbOAQFBAYEAabmUlfL+rkL5NSfHAbqDEYFNFlFfj2O8/qN/PdPY+bg5QB9JDfqkEM0kQDcnPbj5E/PCXvcssc+bibkSHPCRwOS07fFsb5U5CuoK3XuxwEepCD4F6n0QJwH63inuBOgUidNM0e4gZspPtLVdrfCqbMLWtgVe/HPuAIXrbrcsWph1n4oNlHDLrUVlzaojTUN3v7fnAfTAFApAn7PNWr13tZa5BORTsVYElS4gGZRxyIiV7J5qrFSjoVKG9swaQbACPQcQ+pmS92PaF157LuyEoA7VHedOPRKboKVQajMIusn4YdCP4qPMiglAp81gBr3ZsFp6pcBL3bz0JVZKqgTgigzHjs8WkKwA+uuN4h7OO3O6ORpeKmPzMtwh3KLw+3MkaKOJvtDla2pPpIWkHsvr2v1bQk4Gp0Glqz7JLvLdq3zV9h/P5CtAUnCu/69BgWDHkg4VCsz0WxzK81lF4v7LXPbu7T8FHD0FDEZA6lnBwHZN261JVsounpFNYDmEnujETzi/5c/+Xf5s73fcUwasvBRmyvWQ/cDmtNi7ajs5/+Ts+siEbjeB/X3g3e/ZG0DvlgDd6NP2rdxvdX3qXfc2YSIna+uWl1oKolrQrQouVYmDEKT6scQXskFyVpoHxV2nhQH1CYBum0c4/3XIrGWjPT842MBn1o6J2jgz5Yc7C/iWA1cIaBZxt4gCswl++TN/PNec+8Vv+1YJbBBQ37e+ifXxGLedPoO10QhLrTY69UbZmjGkofut950l3AtE00L0OJRVJGygMnmgoyodZKwEh+MX2jj+rrZPFeA5ht5xRu5RkNFX5pLS6qW3+pR/6IkLisRd2utIMJOJDf4wcEMq/vvuuGumsdtlpV186QKOwEWAfgEH8/8Ph/p6BejSbisvcMfmDs6MU5wYxFI/+I0rPVzea2GlRZCuTrZ4h7tl/coMUuU2b2cFjo4znEsK3Lmdoh4VeEKLvcBTvHNugP5tqBTwPRehO3glKVJRYCMGFALArPuXm3V+ThWLSwX3Kt1YZeekBZjmTAqToPWIt2wMbqwlQ1VtndKSbcpQh6DCi5ZCyl94ruYtl33TtwjQPzY/QP/PL3ieOPZy1dbfnKJwvPdnx2PJpBPI8jUK+1HsSVqmmVPqvhWdb83gGUWLoJ0AnbXptRr6LImIIixImz6oGBXrv2ycXAXe70MJluze+D0qxd8oVMd6VaHaq0vM2Rg6wJ4J09YlVd9ivVZgLY7xuo/Ml0GnintZg24icZOOQVCXbhcTRuInojVTf69Ans2Mx4rKq4T8lCCaT28DXiawxmnL76pqFtWZozMhU3gC0erB2V1AatDTDHfdPV8NugB01hZOX95uXn2Q9Q5PZzdCYRVI8GBYJWI0scLPy2YYhHLn377Is6nTwy/dFqyWkU4UHSvpbGGZqJDp4tYo/Mq9APRG/xqltUs9uQYGC+v7yPIXrs1OXbPNDlrGOfuQV82ElXpZhUfk3IzO60EuB4tcJyXnhc6nMV3qpjbsTr9OmCqDrhk//q4Zci+LISAhW2dAoJ6kJuqomXypNzU7xH94v3sGI/WHzKIM507PZu98j31N//UiEqcMKs+akcxeQWgHJVUAN9wXONjsCpKiiYZk0eugYKoKgJEloKDcladDiFBegWHRqhyhosdWOXUBBpa1b/A7UEOCDDFSue8E4io5qqEDaX9pwF1BnkI+/X/9nQD9N+cE6D/3U8ADJ4CdkQqFBtOpCoYGC8UB9tRSq1yyAKDLHJ4G6HYsf9lp576OAyKajnrJKNHZOoXPdfYYKC+fQ6MQBtClvZ/S+QnQDy0Ct7xvLwD9peg2mpV4ZWD83WewJVj28JaAlsUt+Owg02eUh8z4uxapVAkGF4MkBT0cB8+46yxlQFvnvWfQJzsUOE/OmH1eIy8aQmOcHQ/w8GATn984KTOeez0B+rMPXiHg3LP9pJP/ypwZ9Dd/801oN5oCqL+0vg6W3t27voGtOMFiqyX17kutluhfSGmg72ulKpKCaZ6bs334PtHkID2fFAkRX9b5wyRBOCWmM+j+u5cWeVmPB9QYMHCKOg/I89auFQTbygQiK6j090RoWev2V9tNKQdiQoM3RcqK0hR/cPeXLgL0ymr8o//rIkD/R78Fj3wCf/qnf4qFhQU873nPkzcxk/6bv/mbeMpTniL/XllZmfnsv54BOsHY369t48QoxrGdWMDasw8s4Al9ZtGbWPKs9aMA9HCDYMR/Kytw/zDDuTTHnduZOJJXt+n3pPivfzsfxf39LyJA1wx6CewCkmB1DhUwd9ArG6P12tawuuiA65ZX8oInkQprcuXz7lXYRuiRat9MCXynMwG+Ee82USbqqcqT1l7gpVG3HYTtrnimpGm/4q9mp0H5vPuv/+R5ks2sNjeCV6XgbUkNeo7jgyE24xjrSYKdLFMlZ8uK0UF0dWY63qw15WdFTE4AOqlokegW8Hlfoy6getmAe89aQhGgqxOlToX9ps9lvaE6FdxGtWcoafUWyba2Ma7ibok9eb9u0FXWjJkunuvZcYzXztkHfU8AfSLFbtc6AdInAeNjxufTbwzrNG0OeYbGe2cLrblEWRwX1khr4EnclTDTZAB9PFSRuC/sGaBXd/rRrtHXjKzHoAOO/OrjtksmflLN3Zx2B37BAtTLD3kbIflendkwAClK5FmOYZJiJ0nRb9axr900kBmqgock9+oL9wLQm/1rbHVoBqjqTEF9igaiGvUg2H9cRb14acM8wijXvujqkHpYzdeWAn6pKZcB9XHWgI6MTaR5t5oAcx67YvbIJ2x+udOqtrBAz7QpWOcpzqv1See4bSZs3amicRpA0yIiBj/sFKwPurnAUv6TYfvsbPbObd1r+28wFXetIRdtD/lXZaV9HjqArwJBCn+l+WSRyeeahOekukcd2TGoyK2jOAnQJ7lTVm5Q7k8V6ysE6nyVOxqPxGBAM2pgVCQYI0UGtj71DLl2rqC95ef5N95nB+aSUTegTor7b80J0H/2p4AvHw8AemCX5dZXMhXlRHcg/KhOkdmoMgZjb5Y4tgHtkAHiW+GEvfDkwK5JAj1guYMrJtNjT27rakEMrPFvFLTtNIHDS8AHbpkfoP/sTQrQJWhf7m2V7o0uMQ20OGB3gO7PkxbKR9TK4HyczGeRxrLChqvE0JzX4fsqR0D3UjJdTNDRtSkC0V++3wG/D9fp8Q6ODjZwfLiFL2yclmDrcqujAP3A5RZ81QlB9su8AP3Vz/hGycYT4H7x3LpQ2x/a2ga1YxabLWHe7GsToJMGr+Uwk4JuWmLD92lbWGUK8njaD13Xilh7CRBWtlvWvU0Q32NUcNi0PQJhTDdWpKprPbvaRoJy7hNazqO172ztqpNR21vQj6IvQ+Fl7sNsc8kf3Ycz/NEX77sI0GdGVY/fBy4C9MdvbPd85BtvvBG/9Eu/hO/6ru/C5z//eTzzmc/Em9/8Znz0ox/Fk570JLznPe+Z+Tv2AtBD8BtuWFP7jpyT//2xOvsEXXScPn5mE8eGsWbQsxzPPbCAaxbaoua+T0AxnTv3knfbPvU1dwnX0wJfGqSSQb9nJ5XN4dpuTTLov/GpeQH688sMegjQ+b1lDNmtbNALWITfyk3dW4Rkci7iLLmBDpTXxZi7R2KFmdX7JiIEJWuPPcu9DlbHwRxQRfJVPL3MIFYOajl4fi7mXUiduvWn/7GPz+aw8pg+79717c9Dm0yIUoCNToICdEZ/uaHtpKlsMnwmYNc7yqycisdtpxk2U9aW5vLMWnb2Z9cNSDce0tAJ0Bcto8bgDts68ZmgwNWjGbDhWJEqz8/pDOMcU89KM+iFfFbau1nLueq+K8jnNqeUM42iVy4NNz+9Porg/cSH5yur+Bff8UQ8JH3Qtc3aeclfDwg4/XdiAU56kuUcnJgLesZflSoaWJzdbEB53ebtsqSEecpeo0BPcATnc4FRAuzEJO9GGLNtl8kPycr1oD8BeqDiPi9Af/mNz1SKe2jA3DmeoBY4iyVw1u2Cwgz6BE2+HNopsb5SgbiifPrYCkCbGLxgvsjrpBooiOR/50YxzlGTIyFNO8WBbhuXL/SkDpuP0N5OjL/9IiJxt8/XZo0ZdGGqWG12OIRca6S/P2GhidV2HYstZtMjPLiT4PiAtHJglGrGSE6zFDDSgIRAS1OI9pycAgbLOXM9We25gv9AJdlOxOmgDomkvtyCaPw3nWk+BJjTSTagriDMiOS2n/hdcKdbgkusd12bzd65rXt9/ydLgK4WwO6tgGCB7CXUSZirLsjwctEtBebdqCXUdg/1EjwToDvFncE/KquY2z+xc0yGjyfp5wrOde5wfXIkWsjRlPNk9rWBYZFhxNy5ZNAJw6oxs5CyZs4nALrANXk3M+i/vfNrMzn8PnYE6PeHGfRg4nmJzIStCkHw9Dqf8pLk3ocZdKepl2vdQGJo68rAXLXnhnHP8iunxOTCbP0u5mdinwgB+vv3kEEnQO8ZxV0Belj04LbN2V8eaJFQkASShQZvAZjJoXMldx0MTxBosF33brXw1V4iy94CndRmER0Xqb8GGlbyMskoMUtgx2Ng6IGdddyxfhIbyRgnR9voN1q4pLuIQ50+nr56aQnQ+b2kv//vc2bQf+TGpwirhuD71GAotnZ9FAtw9VaSLG1kAN5HaHJkde+vFN8jyZozOEiWGGu8fY8V3ktpu/0oOr4Ub1Nbp4FLCekJNV4z86M0EcC/3G5JQFLZQhHG9IWMjUj/idehCvJ6vKIWoaCorrS4VCZD385BAn55jo/cP1sr06mldfHXCzwCFwH6BR7QC3k4Zs/vuOMOXHXVVXj7298u//7gBz+Iz3zmMwLaT5w4MfPX7RWg+7Y+6ZxXNbh+QuaPmXGeduUnYbsYVjpPWY6Pnt7AQ4MYJ4cxRmmO5x9axHULbVFzX22R2kcvr8pAVANQqQt7joCYjZnzu3cI0HPcO8jEMF1HpLBHgN4r26yVLnHpfnnAkkbRo6zqklbCZR5JLYpUAbrQvU32J6ix1B3PxOMEJJcJp5LWJS+Z4Br/3mqq4+wZEgX/TstTsaXJs9ZtVV613VVE4/iZMvyv9Zmb4wQ/9jezOaw8rM+733rh84QiFmapxVEWxyaIt7MNm2w2Fqm2OTLOC6zHCc5+RXBtg1oFcSJt2TayDBkj6KLCrpuPbEAUjarVsMQMer0mQR7JtJuoHAEFgfe+VlOj3ybeQvVmjgFLIiSDLsfk/fPgR5D6sDOfrC2zIJEp0XNYKX74rz48X1BoGqDzGkuHr3QeA4p1CdBtTANMGNLM5a9T0bRHA97h+g7oIxOOWTBl0YqYXS2w2i6w0lYxQtLWt8Y51gY5YtQwAhkVETJmTQUF2JVdQIDudGaf4o8WQLQlNuGkhuA+LB0Ih3naWfN15+C+pDOfFwUhx5U3yFZslKEQgK6u6/HtIU5sjzBOc3HCLu33cP3KooquGaqU7w7ucag3QYD+3tvnU3FvLFyt7n1Acfc5oAA9wo2rXRzptbC/UxOQfsfaEPdujLEZk3GjmXWep7d80mcH6H5HLEMryEnXljixkgVnJYtB1AnbFbTGM9oP17K0UTPGy2KriYVWS0spuAbjBGvjWH8vbU6ktm4y3mkdLlIM174wF8j8yf4b0RaROGaYpQM1ctp7FOhELdQlX61k6kExwLgYWZac48OzTbEvWsBqtGRNPAs00TThOUL+utShD0vaeaj9fn6QXGnqBM+e79Yr5q7K3YJ5+ab8lTC9gSFyDAV8E577ZxzgKRWZtGa9Pp27+k6lwDOD/u55AfpbAoDuoNf3pum4fPi6/20XkF7+KQDo5XSyhezbXVmDHoLNMK42lUWXYwfnGX6Xp+bPt9eV9yLaB8ygt4DDe6S4O0AXO++hnSAIoSa2AuEiyij3TIPg3j7RbUg4lL5G3EbKvbc3uGCgcYBsVlb9z53irsFuBegiqFqyzdTn8OBAkmeSBb5n6yz+7uxDGLH2PE2w3Ozgyv4+HOz08Q0rhxUw2+eGWTw3QP8XT75B/CYGGrbiWHwP2lvpVkHfi+DZwLAHuT0o7/uDlB3Z5kwbx6w5M/FSomQDNd1Zxn8XhXwmD5hgsXaRVUlPFeHcjMfiG6102ligeJ21IKbOBjPmPF8GJEmvVz0NjnFNAHrmYqkFVziwYNl3B+iffOjYTLZuZgBy8QMzjcBFgD7TcH1t37y6uoqPf/zjQmknzf1HfuRH8KpXvQr333+/vDYYDGY+ob0AdN2Kq36YjhCcFuVGm6akFAZxpyfIKCsmqELSPCb7lLPm+C9ObeDoIMZ6zN7YBb5lfw/X9Ju4tNPCCrOfdAoJmSb6pFTAzv8lBkfqfnP8w3Yix394wNYcwJFuXaiLt3x6vnrgW170PEwD9IqWpIbcHXcKjLCGiQafkVRGPZdbbVCogxSlGtjPM1GqlFPZ7WCayTVQw6xjqkZ3mJByyN/ZM1gjtHz2DfWSfg99cUyb0o5Ifybbi+kZGuCv0LK9Vb+z5OUJE18FWzbjFD/+yfkB+ntvfp70H9avdGqcRfPtKz3qr4EHw2wU/CPNly344gSnmFVMUpwcx9KWbSvLBaAnRuuTqLJvqKLursI0rFGlc6Cq76xN11r1fa2GCEz1RBFWM+k8DwZ06PQ7QHdeote/S+bc5nvZVq1kcKjzotkm7SH/qg/P12ZNAPpOXbKSYQa9cp4qcB5myOXOB4jb/+ZBmmkHdQrXP2b74g6Kn0/DAhv7mjkWGzl6UY5eLUfKOjmJ7uv63MlqOJM2pWZ5mClVV/ugq5VgHZ33QZ83g/4jNz5T7AZ/poUSK+fQp75+dwjS/fcgJFJZsDKYZtndIIPm89sHMfyuEKNHhSnpcj0INtVvT7ICaVbgoc0dPLw1EOok6w4P9bu4aqlfvi8wpeX9Cp1qAvT3zQnQ2WatzO+Yk+pfQto5ndFnH+ziqoUWjvTq2N+u4a71Ib68OcapYY7Tw1xFzaQ3OTDKVY8hsd5ZFSiW3kGlU68BB6tBF0E6fWfozOsUscCs3TVv5+ZsF4rI0daKE8zsU5ZJLTr3Fu+PzjVqR5+IOQngyDPszElxf03/DWhFDWRFhjP5aQHg28U2CeM4GK2gF3VxoLaMhaiDrWILo2JYAnYlsKfYHy3jcG0VhPW0+c2oKZ/TXhEK0EdQUTy+R4FW9ROGsr1u3MG2hEAY0DV6O8DvZ+V5S6B6THaL3BGF6LIODNhpAjrklei//dieQX/PzjtmcvgnMugnleLuAoL6/ZOrsFqbVc339HY2bcQejQpfxqOng5aVu+KmyaefGY7JbwkBemkbp2j5oQszAdCXgPe9d36K+8/d9D9LqddEmy01pyUw300kTkuxtF+6FFhMUY7KbcQYNVUsxGzmVODE3y++YBAsaEtWWJUTSjE6u5HhMc/FQ5wZD3A/M+gbJwV4MoCw0urgmoVV7G938aTlg9a6UWcjVdz/jzkz6G/9FhUT5XUzMCB7d6Z7+NnhUHQsGCRg4IBBAWbK9dk9lkpjRWvPC2yOYxzfGci48iHUf4uKh4FwtT8K0NmnnD4IExmq9K52j8wBftfxnR0pAew36N81hHa/1FLavTyE7aQBM2W3UIiOgsk5zmT0N+nXFMIiPNRsis0jg5X78p/ed/9M63V6bV38/cKOwEWAfmHH84Ie7Xu+53sQxzGe+9zn4hd+4Rfw5S9/GUeOHMGf/dmf4bWvfS3uueeemb9vrwCdxsuNie9UGuvXjZPbuNQWBsBlOjPhzpDucLpprKeZgOg/N4BOGjONyrNWOriq38Al7RZWGuqgimKnOQgOheWsSounmwyzrWeTHLdvJdhJMpwdjiWTc7DbQJZn+P1b/2YmY+Rj974XPbcE6GGggV/vtZDuzDywtY3Tg6EYaaqVX9rv49L+Ag502rik30U9Iu0+Ee+CdF6tJ3JQbeJMmUZxt2OtLzo3ToTKRBETbiRro7FSmUi1jiJcv7oPq50ODvc7Im5CmnE1XoHrHnoqQQpAX65asEn2zLL3BOiv+tv5AfoHbmZ5gN4/p7kz2it0UnO+6FyT/snNjxuUUlEjbKUZtrMMp8YJjg/HWIsTHCMFjbR40uO50Vg03vsma9TeCaLaI5mjy1pyAupF9gKtRSJCSBC/xBpWAiGpJVfVdqHMSsS/Am6R1JlrEEBEbyzLrkErB+aaFVThK4imwqv+Yr6g0DRAn8ig28QvKZdldicMggX10zZGE4ncqQy7+RC725dHcpKD2v12TeuSD7czHGjlqJMmmGeWBc7RaERoNiOsJ3U8NGphlEfYSDSDroRdPSFm24cUidtDDboDdHVWbOLZlYVskvBiK/bJLo633U8dv1AYroJDPrfDT4cU7TDb7mbLhanUJ4swHKdCa39wYxsPbG7Lmj7Q6+FAr40jCwRpmrGsHL2QAl6tcwL0Wz5/61y2rr10dUnFLu2CXRTXEuN/Lzjcw3VLLVyzUMfhTg1f3Bjhge0xHtrO8PBOKqwWuoVsEbiZMrNKdoyVKhkbSnQ2xHEPMtlM3Bsw1xr0atR0iOx6bSLrrNHrpvNMRguDgU5LZWs1ZsUIzMnY4p6gIoX+nVod7sFLsUdSgz6fSNyr+6+XLGGMGF9O78NWvoWz+Zr8flX9MuyLlnB9/XIB6ZvFJgbFDtKCAJm7qTSKw8HaCi6rHRS4TjvXiloC0Gl52Bmdd38sAL1AbGomKoU3OfN47zR3ryDaA7aaOSepnW0vt5FiiBRtAekp6kiEAK+N1iykalT3YLu1cBWPXonEaR/0914IgK6xmzJDPXlt1d/CmPKjOUbOQrNDVltjIO5efoc5L7Ijh4jKfp9+qZo91WFdxd3/5llR+ax9kQP0dgu4ZAl47x4A+tsEoHubtTIULyekBQiu3q5zwYPIouVCUGrB86q4zEdzt6v1IP90eVRlFysVd12/LQtou75L6R9OBTcfHm7hgZ1z4PMXt9ZsZdaw2u7iusVVrLa6uG5pv9DP3V4zg/5/zqni/ms3/xO0yO4Ltgj6IRyf+9c3cW48wpnhENtJLGUz0nZNtG1UsFPFT9U/pk9DxhN9vi9vbEkGXYtEHKBXmgQeEqabxoDnSptZcZYMKX3dP3Og2xX/996NTTluu6Z/px95sNdRPR1J1GhQy5lyUl+eFziXJrhvOBAby8QEhXOvaLflvFn+Q5G4D95970z7xKOts4t/2/sIXAToex/Dx+0IDz74oLRUY9/z17/+9XjlK18p3/XGN75R+gO/4x3vmPm79wrQq6wQo3QqsOO1S6K06jRu289UGbbqe8nNiQasrDuKNFJ7Jta64g+f3sZDQ4r5KCi7vgccagEHmjUsNpjpbGFBosN66azLqYugUEWNopFipHUnA9ao3j5g7WaOzRFzAgW6TZ57ig/fMR/t85YXfet5GfTqRnjuTU+IjvWp4RD3nNuQf/ebLYmEHup1cFm/h8VmJNRQz8S6q0ijSgNPh3KbQDzLsT5OpaZpfczxKUBFdQJ30r6YaaRDyscl/Y5kz69cWsBqpyURVmaRCES1Psp8DQfolqXnNbiSMQG5gGYD5v76dpziNX8/eyuOct4ZQK8yrhpZ1syMTg7ROSYwN90UBmuGeY61JMVaQpX3RLLoO5kGdpiJFbE4Ajrr5S71WkGv0XKC2LXT7eSYd2oqjtSzNk3Sro09kiWzrs8E82ReEIg7MPANWdq9se7VerCLSJyrs5KybM4OP0e624/PmUF/mWXQz69BN0jhvlNZg16Bc/NsyikqWcip+vPy4wHofFTjEmgryLyxN3O9c433oxQd5PLcjTIB60J3b7AEwzKfUQ2nx3V8aasJKn/vZGpAaua00p6oijv7oKdzi8SFAH2iTlJA4SQx3e+rZ1yTQrMSDhAmxuS8IZ50YKdhksO/iVhi4Pd7TbTP1a3REIPxCKd2hhLkW+0t4ODCEpZaTRzstY0yrbXq08f08+R5E6C/f06AvrRyjRKgCWSlXVCFU1p1FU98/sE2rlts4qo+cKgd4dQow9o4x4lBihNDdmOIsJ0yAEM7z8w1lZkdm1gXBkMq1TQ2t1Una1mW44GjMuhm9kzfU2l8eCada5nMGH8WJXLaC9pN1oaKOJIHncs7VO0nWYatOQH6q/qvk/2GQPXu9C5s5Bs4k59BXMQ4Uj+MfdEibmregCtqh5EhQS6K6YTXFGYjDT8WML5Q64vuCintDF1SKK4mQm7MfROcqxo9IT3nHI/k4pW0qqmFLTyI7uRmB0oM9ZDxMsh3MMIYCZpI0RCAzoc3WqtKNiqtlTA4JIFJu498Zg36BwbztVmjivv9nkE3gO57xpQAexmMECFKA1fT7znPlrmNmX6efqNdoGsofDWHqwySBpjWcNtEkMFj5g7S5f4VquJOgP6ePQJ0qpHzx++Pdl5wATZX4af/pmuaoLxUcS/1Ycpmh+ddJhAfzwAAIABJREFUtvoQajtdiC7U1tC/V4C0ep8Gxp11xsCcf95LX1h3nhQZ7ts+hy9snsZ6EuPUeCA+I/f1fa02ruovY6XdxbWLqwbQ9RTHe8ig/7vnPEsy0hYeluOJZ1YApwbaF52MyEGaiD0Sn006ulTMHgkyiu+i/jGTKMe2ByoWN8WQrEqfbHiLQvyzlXZbmI+080xS0Afk3F9qU6iuhmPbQzmuFQjIXrDSYXFetYPQXrhGiYvoknF432gg/szBVkuSEdf2+nKfKKBJevwtd9x9EaB/tUX+Nfz7RYD+NRzsr4evuhAAXSN02h6JtBhVlLUfc5SEmlMaJIIbrQ1m/RGNtUdV+QuN18lRgo2v1Jz/xZkBHh6lKCJSb2o4VB9joZZgXz1Hv5bjULeL/WKM9D9Skto1bTvjDgKz48yeM1tzLo1w94iGu8BwHGuEMYqRZSlu/eLtMxkjH7tb/hkBuudTg21QAqhWR26R6gcFoI9w68kzuPPsuiiWczO8pNfD5Qt9HO61cNViWzYZAj2NbkcCyDfGajRPD0ZCzTw9YO1Rjg3S/4tCgg7S89LU3dtStwT0mwoqr19dwqFeF1cu9XGo25HWTN1GEAyg1TfxN3+W9iFGkdKovoJM79VJmvYbb53diFcAnRR3Z0A4fV23KtZ3K9jQLZvXx0j06XEs2fKTpLbHiYjEbbA2TATGrGWbiU3JJ6XGbVJJ3cE+Aw6ailGxslrBHBRBuoqZ8dwYlfZ2bf4sNa1W38rMobRpslrXTk1bu0lPdvusA3mHIfx9nKZ4xZ/PJxL3spsrkbiGtVnTFRcIxjlgnAbZQb2kz9YwcxzS3A3uB2KGu1stX2/+Vy2vY4ZPnf1FJOgxZxcnKNIU3VaEbquGlX4NBxfrQnEe5zUcG9Rwx7km4pyMF7siT19FkQQiRzsjpEmKO+dUcSdAp2PnmaPp/rAlyDAFfhX6UQojgyqkNVYZ8UlAX41DlRmR0hSjA4ejV5YenJeKcxVzc2tFn7DA+mADW8PtskRmf38ZBxdXZW4uttX+OKNJ3eXzs3w8bwL0D9wxXwb9wMHrtMsCRY6E1aQ0TK5UtlcjSH/u/iauXajjCb0Ch1qF3Ne4qOH0KMPpUY61uMCZMXBmXODhQY5BBpxjRE1O2NskivEs61l93UibyCrROAHUy0CTtQ/SoEoF0vlvz6STFSO2L+L6rlsGXes1h1kGaYVpLRvD72MGfevMfBn0H++/TuDxsBjijuQOnMvXcDo7jTHGuKR2AEvRAr69dROeWL8SnaiBFrN11sAsxQhJMQpgDkF52xTUaavqIkAnmXRpu6buOYHx2CTdEgH7/F3F3TLjpriQSZ+6GgxSCkAHNvMBBgXPro5Y6PMK/lVPXqG3Zyqd4l5u+3Izq7pmvk6A/nuDX59rj5U2a7sAdFnDj+BISebXsodfDaDr2VoW0z4j991sT8hGklmqS/Mx/fg6F1DKvYLEIM+UM6nhds4mGvcs2s8QoL97jwC9WeeOVrWdcDDo6QMpSxRQTh/CwLnpc1YphiDZEFy5dpXRpIuyJTWAVrJfysClXrQnYzQ5UynL63GmAXpN7O0wTwWcf2b9uJaj5LrnEqAuNZo40lsQqvu1i/tLiju/NslT/Mqt//dcc+4VT/uGMoPuoJshMZ71dhwba3Es4nEM2mpZYcWa5D0mKObeIdcZ8XOpZLtVzd27c0xOpHKuFYV8frXDnus1SazwWFSRp19G5iH/vjZURmiaswQMWOk0sdxuiF4OH56CaVhgkjY0j6Iyg84kxJX9HlaaTTx5YUEWwlkLPPy3z85m6x7Tgrj4prlH4CJAn3voHv8PUgyu2WyCau78+aM/+iNRbmf9OUXjWi3GwGf72RNAN4oos7h8ECwSOHrWSWpypK+iGgQaLI8a0gGgsSNwlGdT9iX9msbr+DAVgP7X58Y4Tk+9Jmk2tLMtNPIhuojRZk1ety1ZdBXWiCQbvdikqq0S7DRwQIAObKQRtvIaHkxbYhzH7INbUHhsgDRLcPd9s1G1feze/0+/xQD6bmPvroHytRjpZPuwu9c2cf/mNs4Mx/JgewzSpBabDRzosGWSggIFEZDaU62VzCVyyw2BNVAcK467Gnx3VnQjbLLuNwJ6DWaNIly60JV2TJcv9rC/08H+TlN+J9CUHpyWOdc6d6O6ycajwRfJmlndqQNcAvS3fPaLM22APDcfuw+wBp0eS+XZlXWNUquKSBTZ6b+vjRWMUxDuHEXhTL2ddCy+hxkhvk8z7+qcOzCfzIWp5yWjRPE7a5um81Ep1VqSUajgijkCvB+q4m7Op2EKUSY3h0Tq1uukmlkmvcY6d5ZiaEsWBfLq242SBK+cG6DfgKPbtQkV9xBMy3CaEznx+i7gfFLJP/A7p+upv4ppkRENHFp+bztnzi1DI4lRS1P06jna9QIL7Rr6nUoPgW24tvMGNtI6jo9bqh5sS6fKoLMPeoYhAXqa4s675uuD/sPfcBPq1vO67OXt86Gk9uuoeVkF1yZB3Zi2xBRxWXu4249mXAL6daBWHr6/Auh+s9RDLyGotRbjPCHdcGe0juFI6ZFch0u9ZSz39yvDI9CW8Dvo912Obj4g1zDLX+YF6Jdd8kS5L3QQxf6Y0BEdcq4dPr55pY6rezVc3slxoEW7xFpWAvRUQPraGDg9JqOpJvebVPeNVNcvQYn+VErkskwrKX/7e+n1T+Iki0pIJosP66RgJkGF9HiuwoohmNWWjcygD0TLw/oTW3ZO74w70FqDvjknQH9V/w1TFPdNPJQ9LIJwy7UF9KIOnt14Kq6qX4pu1ERb1VUEtGhGPS5F3TSk3dCMv1SIN9CPulaT3pNz9vwZP6k14KnQ2nesgjyWbgl6JH7PUkTIz8AjQVKOtXyILQHoNak/V3A+DdArYGtVrnJ/HAA6jOdrSTHG/xj+xkx7he8TswB0iw1q7bRl0R8NoHsgK6zwOq/tmi3RaaD+1bwtf7/uGwBj0WQN+b5OQBVz4gezmvaO58v93Nus7QWgswadiQu9L8ZWKkXh9DXXK3BBOG91OgnJzwfoHqeQa+NacoDuwFvsqS5q3c2DTLsz0PzaLZlRZdA5CyOcjYfyoHr7PdtrpixfAXQC86v7S1hoUjx4QdXN7RuTPJkboP+za64WannISGLAXTriGMuP9HZVSldmlcw5+xuflamjlHcyO+m7MUGj9fM6K92+lHHoMvZTiO+x2m2LvWIGnT6IBgQK9IXyHuHsMBHfcJTQtjOzXsdCiy0YNTguxShkYzTqwvRsMInVbGAtSfClAVmcDVzd72Nfs4nre30D6EMJBPzG5y4C9K+2xr+Wf78I0L+Woz3jdz3rWc/CW9/6Vrz0pS/Ffffdh6c+9an43u/9Xnz605/GS17yEvzqr/7qjEesgNLvvPjFj0LT3v2wmkUlHSaTllF0cLaSTFrrMKvTadTEiNDIUqyLzheFKWivuV3QUeoRiEbAQl0jro2aZoEo4EYK5N9tZzjJDwp9L8Jg5xTieAu1bIBaMRaRNYJapbbXJIp6oN1zeC5RTRpO1rSupzUM0cRa1JdWV5LtzxMMk01kWYxjD94zl/Pwge/85vMBeolVvJK02oJ5Hce3Rzg9HOGutQ3cfW5DsmJU91Rg3tAIdJDloyNMYKAZAd3oaHzddVQnQ4MhVdkAATqzwNpijEa704ikRmml3cIVCz1c0utIdJYA0lunVRlzo7Fa704JFBi918VzmFH86dtm75VZsg9e+FwViQtAhGczyMLghnc6TrGZ5nhoOMbZcSLZc843ETKxWnMRmpLaKq3br1vvUXdMNEluKRXPQ1nvZToEFOjjZrjQrJfsDnU6NFtSUpwJzgqrW820jykfUk7A7FOjLkqqQnezFmsc3+VWC9csLUpdO+vYeY3DeIwfnxOg/8ubb8CD1maNTp/f8/My6O59llR3w+3mOfqfzVMIn6qs+RTt+9GMjCv40snneLTTERp5KqA6Hqc4tMiseR3LnRqWu3Wc3kpwfDMRcL6WtZHXG8hb7bI7gcyLiQx6VYN+512ztYDxOUeATsVxCeGVAadJDoA7npIVEqEeloVQvE7tCec9qY0OBsx/L+m1YdDD53OJPd0h9WBA6JlNOLKag1ofkk45RhqfQ5psSG9eCRi096HTZcZI6YvuSOrh3e0LM/wKmwjQf2fODPo1lz8J40ztwNZYS2o8G9hmyUIEPGO5hiu6wGWtDCsN2lhSPCMB6GdGDLAVODUqUNRaKGpdE3VUYaJt2jipjbX8mwUpve+2zgXPCfnI6hV7NpVX6a0l69xXaEfNUNIeiuaE2Txd12Rw5KIJQTsiNsQAutRtBvUCBOgbp++Ya594df+N6EXMemc4m50WYH5vdh828y2xOWy19tTG1bi0dgD9qIU2Ndq5H8gZq/Qqe0snpL5bBpzUX/Ynb0dNLEcL6EZtHKztU3hkgnku8sVqcn5yvUgE7o8MCBKUM+u5EjXRFXBDLlKOE/kQ6wLQWdfO8SU4177nmm02DQcLK1lI3O6D9T83VXcOYVLE+OPhb801drMAdCdFEfxKRjNgXEzbrlB341EBus8fiylWIZtHd7lUP8L9HYIkoNeybDX3rAwYlnRDtc2eQWcgjBR39kHfC0CnintLMuhl/NTCXVqO6HoESm3X11yVYDp77tddAW6jdlsplwJ0BcgaYnNgXokJO1vLoG95XpHNFX2dM1ALIu8fbOCBwYYIxJ0Y7ZTsEAmC1+s40O7ihqVVdOsNLDeqZA3HPs7mB+g37D8gFHdei9dw06/l99JfYPCW4Jy2UJ+1i4HU8FtGnXbaW581aw0RczuxM5TAmmTQJ36qjUCnjQb+yRD1GnTaefrZvE+LTK7Uajg9HGObJaFjLd3sNTVgK2V4NbXVCuiZWW+h12phtdvFmSTG3TtbWGg0cN3iApYbTVzd7cq6ZgadLSjfddtFgD4zqHocP3ARoD+Og7vXQy8vL0tLtWuvvVb6oX/kIx/Bhz70IfzN3/wNXvayl0lt+qw/YQa9e16rsEc/mkQMc9IVY5wdsb2VOjkLDWYLGwL6FqgmSWAtiqDAwJW5rdaIOX9mcJcbmllkUo0A8diAoKzAZ3ZynE4hLZe4cWxvn0Q83gKyIaJ8LDXcCvB0A1hqtKQmSbYiYdUrgCJFbztvYhw1sVFbFJqmgLs8QZxsCEA/OS9Av/kmUUkNfzyb6CS/yoFX07sxTiWT/tDWDh7aHgh9/dx4LFFzOsAiXmQUUv5bHFBjqDFSLcENAjMDguKympPOSCn/zvvAmtAeQSefCdQJFtsN9Bt1ER+h2qfTs1WZXcefG4z3MvWggDplfpUaFycd9M23zp9Bf/93PFeETjzjwqN7XpVq7HTajw7HOBenODoYSRs1BoSYSdcsvg4KN30KPVHZXaLVAtBrkjkIKYZl71XLhrPEgtdPEC1jamMnzoUJlPGOcUPkuDCqzEAJr5ubMuc/x0oFrQrZMBkxl4oOZuIJyOt1iX5ftbgI0skOddryvijL8BNztlkrAXqmm7Beo+UNPLnoKNPBePD69Hs90hPgeXWuQiC/izkIHbYQ4jbIpCkyNOIxalmKek7acIbFTg29lmcgIK23zo0KJLUWxo0OiloduayloIXgBQboP/QNN8l9CemI7iuV09smuogKSplIU+wMxQbpJDGLQQdGHFrLhIhLFbRtn3a+pr8jHDsda/2Ezn9evzqop7a3sTEaIso2EWU70ias32qh1VpCq72iASSvvS5zR5NzITyXvQD0qwWgq5q8Mnn8PjHbo60Hb1wocFkHuLSpAL0otLP2ZqwB3HVS3OMM9XoLjUbXui0wk15Ilw2WN2zT5kuplNZTM8NXcZBtrrsKdgkDXPRqEqCXvdEti6mlVVpC5CKcEgAVB1sDoVo7WtHtBTJwTecZ1ucE6P+6/0ZpiUY2wE6xJbXox7PjIgancAS4onYIK7VFdNAUxXc2XyNwZ/6a7+F/pCFzPGgbYyTYKYZooC490hkAoMp7S/qjUzyO16hjGBcsAyqwWaSSix8UqrHeAbNtERaFJs9vSmROnywI0Clhx72bLDhRlLDsr4mLlYER3+msPR4FqowKr3OPdcQx/mT023sC6NvDSsXd16oKPVp82ie6sM704YEbe5utLn1jmRG3va2knk9hp3IPcZsa/J3/nF7L5XqzEgvNZjKAC3QNoEvnCgPoYYwuzKC7SNxeADoz6CVAtxP1sh7vX69BnLAWvQpsVHa9umjPimsduIJ0BpN4eKG42wDocGmZX6hWXm5NXloggVg9Po/C45ENR5/xvp11eWwm9ANG5f2kDWew8lCnhycvH5TgN8vK1CfSc9Ea9Pko7k8/fImMm8xeE6082O1I2ZsDdc+e78QphlkqpYj01xygc8/g3sFzbdUaWB+PxefbDaD76JaigV6D3mlrqRwp7Wwda+NEXSEel743v//cOJXnflOTY1KGV4/ERjPAyRaTK50OFtstHOz1cCoe4/Ob62BJ3mWdjojEHWkyEVZgI45lj/vvX5jdt9vFTbj40gUagYsA/QIN5ONxmKWlJdx66624/vrr8Z3f+Z347u/+brzhDW8AxeNuuOEGDIdsizLbzzRAr4DS1JYTbEi+qbEGkQbp4cEIDzFDZk4OFbAv7bbFkLE2clxE2CAgZhslEdnQ/paydWa5bFwHW6yT1kw6N49jA6q4F7hjHGEtBWiWaZg2t09gNN5Eno5QZDGaNVJ2mgK0KfTGftY00pJVsrpjmuo8aiKu95BEbQzqS9rjUyLsKbL4HPIsxtmj82XQ3//t3ySA1/xEdQCcQibPXgdkWe6S7FUTJ5f0JCqxnx2NRfjt5DCxSOtAWngQFDqIZMkAgxIsDVgtaUzmaAhdM8JSqyWZ3Eu6XYnecjMhQCdwZPSXWSTZYC1izc2EivEqAlcB9NChEREUH9OgzpkR2//l72brC8zjhhR3qp7qeKkTzmw0f7ZStjHJcffWNk6OYtw/GIli+zBVWr/0OBbdAQYe6hIcorgJQVSj3hDqaqPBDdsEUgjEKeBGdXZpXVKTIIbOFXWyGMAQgOT3zwIVA9KMcwryxXK/BqyLk/mva8Br59zp02xErgC9wRZ6TVzR74vgy5NWV+R7F+s1vG4PAP3oTl0o7mQ3a03kZD20Zm8qOfbKydylPjnIsMs12GSeldKpkLJANBoBKRfuWJ5X+zUB53qPC5zYTHFyM0VaayCpNdDsdtBeZKswd92c8TBRcIwsUHGfN4P+L59KgK5X6K2D1IW07+Tr1pbGA2AMskgWg4ydVkvrIqXkhL1mreWUnarXvmr2qRIGrAD6lOx9Ccz1H7o+leLPLOCDGxs4vbODXjRGOxpjpdvBvk4HjWZfQHqJMna5b+W9rDjuMmd/9x8+MxdQuurIDUKjpNNH1o+Xvngwi1bw+m6Gw80clzRz7KtTeM9agJF9UOQC0jeSDG22PGt30CAboNUWoTjWpG8n3FMycdB3cteUsLljDKzKmVeQrbaK4FKroyWDLv3WlequFHfL9llLJ9oPZR2ogjw/r6rumbJwGPiyelqvIS2KDOdOzZdBf03/TWhGLL/iuSrU3s63kFCIrRiBxybIJl29xdp4qTLnvwm/HRqrbRwXCYZIMCxG2Mi3SpjMFm2X1w+gh44ovrMNG2vTeeWadS+wJVl4AnXWwrLlYQPk/Gi7K/ZgTxAjxal8jI2C+XoNDyj3QO+Hg16tR1eROL8uDVI5QPccbC416H82evdc864UiRvqmhCMMlU6Ih05rCsHz3ScAHEyCTT93P3zvj7c25HXdwmyiXj2RKmI/h4CawkSnBd10/eJKGYN6Db1wbXNdUSAPmDTFn1blUEX5W/tg06RuHe/b/42a2+76ftLsTO/G+7nSeA5yJhzfmgNugak/f1myews9WwVdCv7Qp5Ni6IsWCsBt9qzsL+35U/KMfQgR7nvSGlbJmriX9w+Jw/arXHOskT6KrkE3zuNFg53FnDjyiXi45DjIUEC20fGWYz/a842a8+9/Eq0jN3HZBT9hOv3LUoWmsEAzgn6wEz0sO3aZpyIrxbWpNP/4n4hmkL1Bs4OR7hvY9O0gpyJYsNqT+E48Tsp+EZfpSVaM8oaoG8rdPVaTURymVVnqeTmOBE2p+5XWqOvQeRCROWo7r7cbuOyhQUcHw3x9+fOii++wPIWtpcVvR+WH6lv8xf3z8ZSm7ySi79d6BG4CNAv9IhewOO98IUvxBVXXIGbb75ZFNzvvPNOXHfddfjYxz6Gl7/85dIPfdafEii9+MXQDPojxIMDn1JsH7PhBFApW+eMcGxAKRoa9gjLzToOttV4EDyNSNPMCdCZSdedTgARgXqeS9ZFATo/q4b/xCAXgH4XAXoWYShORY6t7VMYxVso0hhFlqLJLEytiSQbIc7YagLoWPZDex0rGCGdMq8vIK21MW4sSb6C0VlRyY3XJIO+dnS2aGHZZu0FT5PMqGwupdOgjrlucqoOrRu8Coa4FApBHh8bogg6lswwa6352uZ4XIq+Sc9uA5M00MyaLjc066sZVO8RrHRcjvtKSzcS3TjprCpYtaokBeiFsiB003OA4ttksKmGAL30QnQT/VefmM1h5ThUAP35co4KZHTjd4DOzZm09Qd2Rjgbx/jS5gDHLJtOYTyJtPP6WefVaEgGnQEDccRrKqDSJEA3ZXX+7gBdKO1WHy5YrSwbCNtkUUFcnQtuutywqJQvwJznZuMmdbjGj1TH1dgb5rTw3LhhrrbZ776JI/2+3MuFr4CHn//4J+ZyWJlBd4DOLnU69yYzF4Y9SpDu89Pro4PbaHOzsh7uZ57XH/0RDIwGfICIddl5jtp4hChN0Y4ysMilLQrfpHOSGZJjY1RgY1wAzRYiit9wznYpT+WuX5VBL1kb1LJgDfrAROLmrEF/2VOfofoOdmB3N8UZNRHEYZogyViuo8Etpbg3xMnhvwlseN+d3qif1Xtf6TRYltFYPFISdF7wrgrmlXXrVPvnGhkzOJfhwfVNUQ3u1RJ0ogQHpY1OF61mD61mvxqzoDWej6IjhhA3MOvze3MC9Csuu16ykhrMU5aNmBFpY6lBvif3c1zaKnBZC1ht0u60Ua81Re+DQVSWBmwnia5RdkloNNFrd6QWncJxZFUc3WFGCiLqyX0jNhDBbBwHx/lKDtYUQFrrMGbIqR0hDw3OebsvGWMpsdLgno+PhJUKzU6xVaUADgPo0otegDpZFxnOnZzN3rmtYwa9bgDdldBjAckknlOwNENTwDil3vT8VJ+dda8K0DW7GGFUxJI5V4C+aQ32OD8aWDWq+6HafrTRwmJtAQ0B+lxfNSQG1IeF6rvzM5r11ODaejEUkH62SATMa+6ew+7hNwW9ogxvQSid29V/LiLn7dj46bSI8eHRe+ayd28LVNz9nnvZgge+BKAbS0Kyp7HWd3swwdktJei0RaFlDJVAXJCLKK1d0OlUX5uOsdmHqvBiYMqYOa+rPoM86grMR6lmiGkG/Rw5N829kgSCZ9DfsweA/vZnEqDriqkCFGpjyjZrQXBRQbo+7FLtLle/qX1Rv0ZF4ZTaLn5GmT93WruC5qrFqVlcGzMXfvSBVW8FWIvHEnC/f4cU900J7lFPKDM70q430W91cKizgKfsu0QSPQLQvSTNMuj/eU6A/oIrn2DdY1jbTWYecMPKMvZ1WpL44L5AW0+bd3IwxPpoLImVnSRRFmJRCFhmcJ4Anf7oudEYRyWDrv6DsEyCCRcyd2TFRRDtGk+w0JYx0aKlkGTqRVp/zm4+I353qrbPEhHCFLNuCgwssFvQSruDyxcXcWI0xK3n1sSO17JcbOqi3FWK8CkT4JMPPTzTen0E9+DiyxdoBC4C9As0kI/HYW6//Xb80A/9kGTM3/SmN+Ftb3ubfM3rXvc6nD17Fr/7u78789eWQmcBQJ8Qrdilx7EbUkbrmAE+PoxxQnqKqyIuqdTLTbaD0IU+zCOs5eyhGmFI8x1xk7JsBoU02Iu8pT2mD7RUiOv0MMdOCtwb17CeM5PCmuMMW4MzGCfbsrORkt2q03g1MUi25MEOuw1WMEZU0G46bkJUb6Pe2oei3kbeWEIR1YRaWTAiG58VgH7u6JdmMkY+du9+7lPFeQ+dYI8+04FXepTSsGkwaVxZkyntLoR+BK1Bj9nHnPXy6vbwR+jnbPElFHWNTRMccIw4XtJv2yKrzBjzOzqsyad6qNXvJqTKC5jQ76WqvWYOQ4Be3WgNrBhF3ERRvO8wz10xlF4tAcQPfeyzM40bP1fOO7ZZM4Du1+wRZIao6YSR3r6T5riDrem2Bnhge4BjOyMVHiT4btQtQ+BEUX1dxaC0lRw3VAHoxiLgs1P7ZUzL1llaTuC0Pd0MI1FcFy0DEYShy6r3UwX2qMSvLATOedYpk2LH4IDS7FX2Rpw3mwP8XL8W4f233TbT2Pm4OUCnk+c16FXe226RAAwNGpUq7Q42/PXy72Y63Dv0X8tWcr7qJ02Mu3B1OpYEraOxBM4acYxanuHAYg0L7QhbgwyDcYZTWynWdlIUzaY8Or0Oeos9sI885684z8ZZdWAuv9uFiIr7HgH6Dzzlm0qAHqrXCxnTShlOD3bEttE5YjCH3Q/Yd3y5xbIQDYBxbqhgm9UdmlPG+SF1iAbMXbdBnxXAi3il9KmtdB3cfngN6Jc3Bzg7jHH/xpa0VnOBvSsXF3DFYh+tRgudJst5qp8ysCI3/fzX+QoDTf/9ztnWrM+7w4evkaAVH0ui10BFdwXsp8YE1Tm+cTnCkW6EJ3TqONiqo9VaQKvRRZ6PUeRsETnGKBlKOzM6lsyC7et0JVM7onBckuPezVQz6kNIrfQWgwIEBDUTvpRAZ4HtcYokY+syKoyTKQVZ+9JGUuwCA3isf2b2PkPOevjcwa8TdY3eK1l0DQwJDd5Sgez2J+uXdjvPcObEbHWZPnb4ZauOAAAgAElEQVSv6v0kIDXoVYZZLUMFfT1nKT2MnY0SvIeZ7mZUxyAfYqPYxKgYYiNfR4oEcTEwCJZLLTqV4Sk8d6h2AN2ogwO1/dKKjdXttE8MyvKH95CjORYKfI5j+RY2ijE2hQZfZVFVGd7AuQWjJNMqQL2itCvoqMC623UC9L8a3zKXvXs7VdxPATujKoMu9zrIYstYEpwZyCVATyyD7ufN7LAa4uqh2V2zbw6WJlLjwfttSVWlXvqCfKd9r7ADPfZjz13WntcB2skGwXkG7PDcWNJX15agXPd+DNV7UZG4S5aBvQD0n3/mD6Aj1GVPGOhakjGx++tyZSIWJ7bJlPpLtyAMW1T/9uCGU90FiJf8qyr0ydeVsVINvawqHr9kTelcc4B+/84mjg+3cWo8lEeZUCi45mP0mm3s6yxgf3sB1y0fNoBOK1Dtd6S4v/Oz/89cc+7mq6+SrjmiOSLix8CTV1ZEVZ1gV30I9bke2qLg7whnR0PREvLgb7dJiruGLrjm6OedGgxVpNj2Ce4DOi6qH9Qk8JZ7o+V6zvajaruWzKkvQ/tJ31EE6lh+R12ejHuPiT9KpyT6QhxjiO4Q9Ye4jz1haQmnRiPctr6GUZJiazBCLS/QtvnAecDr/sKZ0zON3aR3cPG3Cz0CFwH6hR7Rr8HxRqOR1NxS4X3Wn0mArvU2oWc3vRFJhticWEbsaHBOj/RBUCcPAvCa9pal6A8B+nqukXvpXmkquox48n2sAy4BepvZAyp2s90NcO84wkYWYTNPxaBtj84hTnYQ5REittpgFoYOS7yBQbKpitHs1lrjeShA52ZXq3cEoKPeQq2pAD0VgJ4gG68JxX3toXtnMkY+du96zpPFiQ9HzrNpNJ4TAD3SzK4bUVWrZos0glDWtKqxdbVhBegKMklb57G4aWgrMM1ws+Zc1IpZh2qAVQGmlhKMKSpC4CjOqWfsXOPXKLjWKsVbkvE+Om1NgwvupOqzb6VUcX/5X88GMnmE3QC6+kiFbNh0BiluS6eFqu38nrvWt/HQzhAP7wxxcjiWAAsdC2mBZd0CJMckGxMDIRoIYvRa2AfSv9xYBxIgsiyabYYKDs1pNQVolktw7AWEyRhSqMnF+ZRCJu3wjH3gzg0ptqyPcwfS6/mdOk9npVcDfu/z82XjFKDXMEo5PyoH0+5SuYzd9wkz7Ep1N9qvTVqpr50yHp6JmLYpYSBKj1UgSjNhw+RjetAZVps5OvVCz61W4ORGgvWdDFsxHQmgTkV71tZ1Wmh325bl15NQm2MkTCcn2El4H3RpszZnBv37n/x0WSfh9UqdsgTMNDN+ygA6aw6pH3Bkga0Ju1LHx4cyV5Q+qKC+YstIVsrnA8GLZUocsHuLQq51yWLZ332cvTf3Q9tDKX1ha0a2VjQIikv7XRzu98DWSV4j6bjDb3xQ2aB2KeDe0on7/TkB+qHDV4Mkbdr4y7sNdGsRxomKEB1lux8B6HVc3ovwhG4TB9t1NJo91OtdoKDjGiNJx4jTkdSyDxJSVRvoNtpS50ybv5UUeNAy6OtZXaju26TVFwTretcWLFsai/I6hUq1LR/3GK5O1unTXnaaVofJrJuwHjje7g7rXFM6LwMqOid4P4WZY4hPAIR6/QLQzx6fD6D/aO/1qBlAd9jrNk+/2R7Wd9p/98w27Tmz62w4OipG2Cl2hBo/KLZFIX2IbcnGj4uRiMYdqq0KUF+NVtCOWlgRyntL6uCZpyd9ngCjS1J9VMOYwlXIcTTfwrlihK0ix6CUE1MWgouueQadjjz/E4DuoM5Au0N0nZOaQf/E+Hfm2mN//i0G0IeaGRcBuIBqz5FzQU8GZGj7E2aohe1RBRVcOE7KUEz8a9LjsRUUZNflFRdGcTuk20T5o4GWqge46bWqjaGCtlHcaxkQZUpvZ3mSNGpoKoASgF7FvmVusl79sn3Au/bQZu3tBOgNrS3WMdPsrobo/TWdgU55V7EzD78G9rj0Dh18W1CkbJemgXEZU6e4W8mOl5Q4SC8NsAdHPIvPNVoU+PLOOh4abGE9GWM9HhtApxgbgegYiy3awRXsa/dxxcIBBeh0CgMPlgD9Nz73x3PNOaq4O12dJU38YXCUGXE+NFOtfhL7obP1mmTQY64i9bNcJE4DucrEo96Qa11wjKWvuQ0G92UGfyVObnbI2X5SfmcBflWS15FkUoc2kMcm5V6siJRbqD8ozLoasL/bwWULWmZHivvaeIy7NzcwiBOc3d6RhVI3hUXxvVhesHZmprGbXBUXf7vQI3ARoF/oEb3Ax1tfX8cHP/hB3HvvvXjLW96C1dVVEY47fPgwjhw5MvO3lWraouLeCETAdjmUGV5RPy9ynBnGQqthTSEfpF6TzksQQ8PI1lfnYlWLZXsz0hS32TGNTkFTe0SvUEyuFmF/k+rWkAw6qYXb4nBFuH9EgA6ciVNRgB/GW0iysQjn0Mkg5ZDUwMF4HYN4HY2INEFGO6nKTXKttlohQG+0V0SYqNNaFBBIZy7PE6Rjo7g//OWZjJGP3X951vWSoZXosQm1eQZBWjJZBp3GlAZdNjADCKr86Q+tc6JjSIeJSsMC0KWOkv1TqTacYX00VGXOTB1J1qSz7lqcAj8HKSHQHW+UWBuQTB0boUtau6FSoVzqtEmHVyBL0Gt+lRykjLQbqJDfeV/TFK/+1F5q0J8vAm38Dq9dIxShM77Jtn1FjlMyx1I8sDPE6VEsJQDr1vvd1Z0lu2Y0fF4DwTGdNM4xOgYUTXGhp9Dd8IyFXqsJfnFjNWDODgE8htLzqh8BZShEHLDbaGp9vo2ZZNbZZ7nZkIg2I+rS6i+xLHxK6SsV+fvDO++aa86VAD3T3s6VinvlnujtN/aGOX46Rk6AMEfKPCbu96pfXeFld7Y0ia2ZU/8GTmOyNyRMMhhJf3OMx6gXOW480sHBhTqOro1xbpDiCydGeOhcgla3jWa7hf5CF72FrhWNKt28pPpNAXR1FDUTQIA+9j7ocwL0773haSUQK1Xn6RRFKv5GavvpAZXTYwHmBzodXLO8hCsWF6SUhSI8HqTUMRZ3Sv7HIBnvh8WzJt/nrdzMgVIAUwUkfHZpppLiedrf9uHtIc6OzPlLEiy1NUigNdWW9Q3mpmYE7d7a3fIMIX/l9f3+Fz4317zbf/gJONgAFusRnrncwkqjhvXhWJg0d2+z/WGOp6+2cEWvjicsNHGo00Be6yCrtUwdPEGeJUizWNoCMWAjbCLuDyI8x2wVReRSCbS12hRVYws+Fc88PlKQvdhUUbp2TZ3bozsZtpMCg4y2g/tKR0q2eg22tWJAVDsusK6XDxGgM7sbE7hTvZ0A3YA672CpYULbwdgRO4zkGc49PB9A/4Huv0ZDaOZGmQ+ArFON3caUIMYdd8myOc1dJNeQFtRXJ4ebQm4jbObr0mP9TH5G7PhqtCT17N1aTyjuvagve+JC1AMr3Q/XltCLWlitLQmgJwuBe+J9+QbO5kNsIpPSMv+psuUOegnMNdOq/1X90XXN6u/l54sYt8a/P9e8+0//Brj/JLA1ADa2FHw78BbKuzDJlE3U72rttpdeaPBM9z4CdtalizSGtTjz0Eho17zzp2p7lA1SfJsQeQ1X9ubfpWRPxBo1WMrP89+ZVvxIMoGPPAGysbVOZalFE2h3FKCLjeUebgEGUuH7beDIfuAdvz1/DfrP3vQDZS11yeyzPvZhkIOD6L8rUJ+wbqWWTrgP6pjpnuDMN+9z7pbRmQ0i0GpZ4sq30Gvnj1SKCSuPyZgU92yeldZqBLAilJtzrrM2mq3NxjjQXcI1y4fRa3ax2t2n427n4rZ1nMa45fP/71xz7p9fd42cC/duMuc4dtTLEf/IfA3V9bH2q2TSWVtaF49lAJXvEZ0hssvMixCGhInx8tgK4DVEo/uHluZwTOmL8Flagkr7PX1eaFDdnXZTW+FSJ4fHsq2oZLtIqU+jjsO9Hq5aZju6JvZ3u1JmdHIwwMZojAfXN+Tcub+WgZoix4Mb52Yau5kByMUPzDQCFwH6TMP1tX0zKe7f8R3fgX379km9+d13341rrrkGP/MzPyO091tuuWXmEwoBOgFFmFYKM0zhgWkEaBTWRrFEA1n3K6JdRo1mplsywlTlTXLJbGxKDToBuoLKbqspwOxQp6XU9qaKxO03gM7MIDMnXx5pj9zjo0yy8aNkgCRLRDGUBj/PY8l+D+MNDONNNKJcal5Jl61HzGCq0xXVuwLQG3W2megbQOemnSAZEaAnWDt2/0zGyMfu15/1xLJVmG9SXpPnPcq9N7dktq19mhtCobwaCCbdkOqbNNJt2Qy0F7M4/nVt63FmsKO1r0Sx0tqrWQohydia2qdslAToQnH3WnMVFXPgr+fjG6fSjL22W+meniG0TEnQ45Pfzsz2a//u7pnGjecc1qB3BaCrYyPHFPV2tlfTzPnpcSxt1VhKQXDO+nP+7graMgqs27UJS8eaGyMBs1DUjdqu9V2Ve1E5wVWChNRSvs7giLZdc4CuTnWpfmsZBn6P9Eq1Nn+edVYBm7rWrcexZN+zLLX2YVR7j9CvR3jP526faewmKe41oUpOAHRDhgoZlXMZ1px7MlDmqTmVnr2TzG1AaZdgTzheUYGmOdw5EU0J0FUUjqr0DepCRDmuPdiSNmr3nR7h9HaKL68lOLmdodvvSsa8w0eP9GwtR/Dab884hRn0Cw3Q/6cnPk0dYpvbMvcsuDI0McCN0UiYKpf1e1jptHH10pKAdTpLtFt0kmLJqjidUINaorbbqqow1fF0IB5mpEy2YCKPX1lZvnM71o4BDISy6wO7PGwmsbKUpK56UoyyBH42zyWDEgg6+tEJ0P9gToC+evgqAegLdeCblxpYaUQYjklbz3HvToaNNMdTllu4tFvH5f069nfqyGttZDWSs0k/zZBm7OseYxAX2BwzKEaGSg1xqgCdQTcGY8mC6nXaMid3MtLfCxwfKuOo3+Aaorq+qnmcGLKDA7ss1KRmnUEzOtP9FimhWk9O+8KM/SBVBXRmjLXmnKromoFnsIBMI6e0W3sGDajSxhCgH7tzrjX7fd2fQD3SkgRfk1WttnOS9C6VegRGA1awo0rvGs6kLSGcphZHIgrpw2JLKO9rxZpk2vvoqDgV2HqqJjXodVF7p0Z8A/trC+hETeyPltCJSHvnuTVwLB+IevtaEWNb6uNtf7Jsq2jNGIBzUT7VlnfqfjW3S8q+ZG0TfHZOgP6Lbwa++BCwsQNsGkB3/8SBHYk4dGEWekCHpBwOpMb2FABKZtYAOjUsybhwoG8Cbx68tKqkqgOIpcgtFKcA3UpvJCBmnTQcoJPMyGNIxp4BBOImdibgIzaAbp8vqJvr2XzOUwJ0q1fvd4ArDwLv2EMG/d/f9P2lirvnal0ErqxJr/gbpZaGhlbO9wLDuel/96BguKfqPK7arVU16BMczfIGefZ+PR5hJ43xwPY6Hh5uSkAx4cBxEPMUKVkiWYxDvWVcv+8ydBodLHYWzY9xG6vPbLP2O3MC9Jdce7Uqshe09cooEF/Myue0VJO2mBo4+uwMOZ6zt2vkeG3FqWgM+ep2NgqP7eDfu/U4+Ffgr/sKv8uTPt4alDaO61tYfSwnpU6OtIh0hlyEgiCfmfd6DQe6XRxZXBCKPFlh9CVZN789jvHwxiZSKtCzdWzJKMpx9CJAnxlTPZ4fuAjQH8/R3eOxKQ73jGc8A7/8y7+MxcVF3HbbbQLQP/GJT+AHf/AH9yQSd8uLXyx0nGA/K02zm2ivq2GtNOtdCJS2E7ZPUpPtNB06V8yskpa4xmg1FX+pdskoHwUpqEDJfoyNOq5e6IIA7UBDReJWWlbTS7opAfoww3pa4EtbmWRnKOBDsFlnLSKjiexhnuwgjrflISIlJhIiTrcAjBpqjS7q7VW06k0st3rioDM7S2A+NIB+7sRDczle73r2UyRDW0agLTMjdHTLoAv4Ce6/G2g6o055pXGnU9mlgq/RsQWoSya9hlajJoGPh7c3RWiLIlKSATRwwQwPN0HWy/JespWQdLXNM6nX1/xogU5TgQSjr0qn0qCBEewU+BqgV1EWrUdSapxRGQ0ND7/iTP+bW+fvg/6Bm79N+nYKlrTyibUkE/G5L23v4Ow4ljZ+nD9Uft5J6YRrv09Fu2QdqENDZgdrxXktFKCiknu71dFgR6NhdaU6Jp7l9ICK18epmjuDIxrB7hoYcpV3qfeyvqJajqBOn9LJtAWXtMGz+8dxjKnCT9AvYkE1HOx0BLwvfQXA/7uPfXyuObdbBt1F4iJSCoOsznRWzkG6tMUSn4LUikyAENsZimNlrN6GtZfRcocCC9I2K8fWKJWgQ7OhwkDNLEa9KLCvS2eF463T6c5jI5zYSLGVNzAq6ugv9dDtd8RhYN25APFKY09dQkPpDtZVrEhP6EJk0P/59Teqw+MAV7IZug63k1g1BszoHei0RRSOwn5cV84uYNCFLXPMFZIAHesSmdm+pNfV2j9PZZTOrx108qnMdrt58ICJC5qxVIiOPmsG18axCEpSPImBN9qDCRxiziPtiGSUnAljBxdKZZbhf9w1W1mKB4ZWDl2LlUaBfq3As3o5Vhts05VJOc2puCblTEd6day0aljtAEtsKcVSj4jrT1kyzEqRBro5zrE2ZOCwhjSPSoBOVgYV/9vUMuk2BZpy3TMYdXzA99IxztGsFTjYYxAuwojHIKMn0Sw57SOdVrb7ZJCNdZwLzTrODBOcGTJ4nGM7pQhljjEFmwpgKOCc56eNoRjqJRhl3kvqOLlW2Af9+GwBSR+7F3VeiXrExqIV6Cn3Vgv0OE/Hs5AVGNK/qFCcZgpVsZpjT/AisqdgA7VBsSlrVJuxSRjYiOhu4VUSjurwZKEdqC1bzfpBya6TwcTW3EfzHZwpRlKbnggANxq07M0K2gnfVY9eqbX6/7xE54RrYFcz/glujz84l737968DPvsFYG0D2KIEDdtLWpZa6bzAyjLQ7wELfYBg3UXjCNr5cFaLZ9CZbCRAF9YEM9wUOjOXwWLG5WecnaZ7VEWdN6JAGeyk6DcBdrtN5obuDXxPTME6AnM1tQLaeQ07A+Dchn6/nIMBdKlFrgMkGV17BHjn786fQf+3N70UjZqXW+mMkJlh5R16xyZLfviaPIIaxzLg5zAzSKXr3JyC8862ClPuEtf1b9QD+R6swqvs4KA9z9fHA2wmDJTGGCekuKeiGSQzKipwSW8FT169Qrr4NJq0zZXoo9vSPE/xB3fMl0H/p1c/QYLvuifpuDlA90AEgTl9CdLGKQDLfZ8+GP1jZrSZOWcZKB9rQ1oSDc540Ih7jmqWKNVdMuhWrkggzTF3CrxzoiSh4mPKGnRm0IXmrnuYCtJp1pw6OBlLseoRetRPabdLFgP3ODLixmzRtj2QZALtsiZmVOvoIkDfI2i7wB+/CNAv8IBeyMOFfdBDgP7AAw9ImzXWos/6UyqRG0Cf/rwalCqTysVLyi7rpUm/ZEaC4NGpljQxdH7WxQEqcDpmBl1BFTd1OkPMjCx22tKa7NrFrrRGW5Fa3ghLTW15pa4H8OBIszJf2MhwZkyArpRxUtnp8KXxBrJkC0k8QJIMK6EWoaUpaOKuR4DeIECvse1FVwylAPQ8wXh0ToD6uZOzKVb62P03AnQx5FbbZQ4/x016BhPwWibfqVdhBNXVUjn2BNUE6F7jSuNPIC2/1+sYZ6koOmtmVqmZDvzzSEVFWGvEyC5BuLSY08JzRPw7AToNt4FzZuKYP1dAoQ/J9lrk2EXuaPx5DCFAlfQ37T3+5ltnq93ndVY16N+GvhR76p7FjXCDwCPPcf/OQFqInBqNBaCfGSUC0hkUYlsRmSXWK1T6fbINC70eAct1NEg/73Q1s2mZdBkToaO7w6sQS4I6QU95zkUR4iOFzduwRVqjzPGRezoF0DnP+F7JaNsxBYDmudwLAnIC/kMd3h+WdER400c+NpfDWtWgk9ap51oCW7tXXpNZAj+rF/e2cnQACW4IOug1EqSQ5uqgQYC3qEcrdbsuAF2vRwA6a+RkjilIowu41GUgSTPinJv3nopxZjtF3CDNuYHuQlcz59Z1wGu1HaRPA3T/uwN0tlnbK8X9xdc+VQ5X2TbSnjUQRYVxBqXIXuF9pPL+YrMpqumr5KKaY0Tnaythh2idRwyIMStBJ41169M00N3ssgc0nY5evUfvgItded3sieEQZ9iKMWaJRyIZEGZNNNur85jzwOe71lFXxbNaI0xQkuEP754XoF+DhXqBXpTjpj6wv15goUYoS7YUs9wcswgLDWC1S/X7SNZhnSwfA5d0Wikwuj7KcZKCgWzdJSC9EFYQ2wOtdOtoN+tYYU8qiovybxlwcqCZT4JTBmkP9VjOYv21mGlndpwB4bGyDyjqx/7BywLQGzg1iAWkc09iRl4UqyMKO0KCC8yiE+TTUua1puxBLNHi+5hxp8Dm6YfnK0u5ufOjaEQq1hX41waO3P22EhObWSX7xeaZZ9A5lmSKaVZdNeypv8IsNcXiFDInE0Bdr1Zz8Ra2Fju1WOuK2vuR+iEsRn1p7hZFzKQPca6IMUCCEduRWpcWzjjpgGI92atnnV+KaDzX7JXo3P9j3DYnQP93rwFuvVMBOkEtQW7LstSqcwHsX1GA3utoBl2YYiwXk/KwioJOUC7t1wiKmdEmWGZEQgL7CpJlOy9tatA+zS7L16Q1FbA2lxW1vdVSur03+KBYHR8CxFMNDPAceC1r63YOBOhOc7e9iGyA6y4HfuuD8wP0txhAd1aSA/RQJC6EzFX4yAF3OGOnULjLM5ShypL7VJqz0Bbqv63jh71DtTi0hpp2+ORwC+fiIbaSEXaSMcbJCOOUAJ2JBhbua7nTpX0C9CtRr9O+sHSkEsb0M6ao6B/+w5/Mtce+8MorRGjSZzKfJZsdsJK0nVlNWFai1m4ldd5ujUKj9JcloTUm+DV/zUQWwxp0Z4opI45+HwG6UtpdF4ND5kECBlpJpVeKu7XJNYCuHWwaaDYbSKmhIy3XWHbHgKeVY/F66KumGbZZNpZpS9/K389x//pFivusmOrxfP9FgP54ju4ej33o0CF86EMfwjd90zdNZND//M//HK94xStw9OjRmb/BgdJ7X8Q2axplLfdYixoSDNIAMNJH4CYGRxxZtqVRJWtR8zVhr80cOJOy/jzD0YH1rTSRDVocRh0P9Dpgfe+TljSD3jcQRMAoYMMonCfjDFtZgVvXEqExUkSIzlQzGqMeJcjjdeTpJvIkQUZxDho+0sqiAnmNoFSzqRQpardXJTjALLo4qgKeUgH5rIs8d+rYXIb81571FHESJbJuGWbJMAh9UpU2HUg79d3rzsVhEhCkkVcKAHUiEhRNSbwUBdHNgcZVNQAKaVnF4yZUJxaBH60XJACXY1ngxHPjzDgxqKHiJlrXrvR2rb/l+UuWRFonmTqviaaVVLZKL0k+SzXSV//tbBmlEKC/9+bnyzzQiLTOPtJPeX2b1gf92HAkgOSeDRWJOzsci/aBZ5QoAEc2BsecNC26q5yXrVYbiwuLaFgPa9n0TEivzJCENdUVOiz7ovt8dHqe1npTtd2CVt7+yRRTCdApoCUZrEyBOa9vqdnEVYsLMtdXTGRslCR45Yc+PNecC1XcmR1SNkkKOiTJKEaWkmquegOaCbHaSNLymlqT320yqk4Z4VyU13fiAmeGOm95fRwj1skrbV/r7DqsgmGWcWzCg5lm65c7BOY8tr5vY5BZjbHWDneXF9DudVH//9h7E2DLsqs6cL95/GPOWZlZg1QlqSRTEhpsCSFACCy5GyG7aZvBRhJWg9wYC7vtMG07GuN20yY62tDtwEQztCEEBCaiEQIDahCF1Y3EoMESqlKpSqUqVdaUc+Yf3nyHjrXW3vfenyq59V5mERCRv/T18v//3n33nXvuOXvttfba6DDAdUbjxznH2t+yRKFMNPisqzDoAOjTG6xB/5rb7xazFey9s7pIvmCNw+8Bstdabdtst+nN0fNShoJZ8kUy5I64N6MsAo+FaqFwLnYTx0p9eARCYeCmcgLMXQVbdPIuDAnNntofG0A6GHR8R51jyCBV2iH2hA7DXuKBU40gk2aRaWK/+sinV5p3W0fvJJPVrmV236Bh282aHQWLXkNbuIygmEF0ntrhQZPz4nC/b1s9mMQpAQf/iCvTxM6N5vbYtank1/SMUHKr16zboT68HfAox3G2QMtyuzJF+7uczsP42u7qeXgvKIPC0fjcPnoCp7x2uB5wUsY6cWk84zdYJvYWRtKsLSM6JAEA8C/PMstrDcthXAdfglzy+vNTqBYS+8zjqxk7vr7z7c6gU9MVM8hVGR74V4A5kzTXAXW1WROTLpFKMOlIWuArup0jkT13fnvO+tUUrdzAq+fY+cBpw6wKDd7w+9xO14/Yen1oJ+tHbb2+ZnvYYyyjYdxuPqNBHzQjsCgFnuU64f7tIZ1WBavWP9292pO0Zq5eg/4Df8fsYw+IbUZOCktCt2sGxnpjTf8+vCnGGesWDULbRiUPwDy+CdYdeBc3BPbPudlkpD0aezVERM2Ozh6xBpOE/nEqhHKxruIf7lFaxYgBRXmgAPQ4d7R+m0ylBMDjzr7Oj2y+M+t4xM8A6HefMfvVD64O0P/BK/8aGXSHxiWDXmmzpj+WLHoJQpXMiT2BajrvPsG1yUvkIjkY7xFrTvFzpdSq0FaE2fB8bNMUa9qUj2hxCSICoBxu7fPFlKaS6BJCeFtvWF5v2onhtr1k+3ar47PV0Z+3jCIi+YlyyN9YEaC/6vgxetlE2RoT+d56MTogDNstkiFwRg/jOLiwI2bGWgSADuM4rF0A0qG4inaOETNGMrWUzwukcw56O8uQvGNNw1p5cTwhSx/xYChEQoXTbjet3WrZHP3aUW6B+U3TUikV0FEAziA1tJBbSGVJpSTva3tc+ocAACAASURBVJTkZfbIlctL7RNLA5BbL1hqBG4B9KWG60/3ye9617vYTu2Xf/mXaQ6HmnQYh73tbW+zN7zhDfZjP/ZjS5/Qfw6gR+0kFkve1A7UIe9EtlNO5OjTClmOGSATAp1rqdm5BQKx1J6eLMi41XNI8ZBhrhNAot0DZIcvWuvRCbjrvboL6U9dDPolMKZJRoAOgyAwNNg0e/WZteuJ5Qvow/YsXSSWAaBkMOoQQE/r0ozVwUg3utZub6o2nXIvVO8JoKeLXQL0iytK3P/1K18igB49kCsSd8gtg0GXqU1Zb87xdQDFhZmMZd06DqxVH+79Q30jJEvPNk0APgrkIykwdyljsMQB0JEDVvYXEng4a1cYZHf6lOc7ghGF8iWz6b1MwwTHAXow0ADT3/0HyzFKzwnQnd3FSSD0xucCi44EwDn0F10s7DNX9+xJgJTR1C6h5tnb2mA+oe4+nLQxLxFEtlptGwyG1zGKcjYNU7XI6HNrZ2zp7KXLvEtm3V/H3yuZIW7Ima/oE89rKAdbyFDBmAcLe3rYt269wfZUuBfGi7m94wOrA/SnRg06AbMPOjLtAG1JarPpjI/uwFDI6pCgwefpwZCxiXZ88CugbpcO7KMFJMcC5xh3PpfHFo+HMYN8HRv4dO6txRyggzmXSZPGY2eS2WyhTgxI5A0316w77FkDySPvRiAZneTbAupavor+5NHi5zqJ++QGAfpfOnVX0WJI72c24RqHe0lmYoc84AJ7DmCHQA0MRDCQqlV1pYSXNbAUBYqXinSfgaqD9DCQLFs6Rd2xh1QeYUXyR+Uk7oBlkHeP7eJkSsNEfGsdkKdHMDdhWoTzlfqm2vRIawfW8V/73GoA/fCxO2msCR7m3n7DNps1O94yAvT9WWoLSsvB3Aqgr3ccoHe7RSnDXpLZzhxGfImd3ZsXulh2qmD3g7pt9ZpMIm31OlKw1NXBYocAHYGvGlxvdBAg1+3oEI7tiJ61Nj67P7fdWVLUiELhAMn8lfHcrk7nvEZIYmDdGHTQ9wOlWJgHFYDe6lBAPq3B6yG3Z8YwtkvsDx5ZrUXd6zrfRgZd4KWE3vpJSdKSP1cof5BBD3Cu1Rr7hR61CpVFStHyTEAcwFh2bUhgC6jj/1G3DoCNXuoQsJ+ob9tarW8nG0dto7Zmc++Zfi2f2R5d4nOCdJi9AqjrWFF7rrnMd2Zi38GUm8whyQJ2f1WTuH/635p94iGza7slQEdiEkz3GgB6x+wQJO7wnfS9Cgw7WuXRzX1R+FFq/D2DhjUfzDYAOuYNYgsw6AToiGlQO+5ZuXgsgG50nKA5ZKmKCCRcJTvUilCydoi/8J6okIEjfSgCgtWP+nY8D2qAF5w2+/X7Vwfo3/+Vf42dEsrkovrXhxN/JHCL3c/X3fL85dMAdhtAU0lM7dECrVEmF6mYShhaoc8DOIbhavjb7DhAHy3mNIeDpB1r1CyZEqAnydRSMOix+jpjfqS/aXdvn6JazpiAOAjQCW6Thf32Q6tJ3F957BhVi+FdU01CYJ3Bz5udDpOAMKUkWPf+4xqnTF5MII+YpIkRVYIf41mOI+aqxrE0m/VyCm+HSKUqPIa8XO/yZEqAzs4jfi3Kkc+t2Wpas9m0ec1sBrUpyB0vX8T1hLy9gUmQZlZboAWl9gaVMgrIP3Tplov70qDqeXzBLYD+PA7ujR56Z2fHvuVbvsU+9rGP2d7enp08edLOnTtnr33ta+03f/M3bTAYLP0WhcT9zW+xjrcKi60mAkVkNFV3KwYd5khYpOEUS1bX2cTd1OxqktuFRW5PzHI6u0OWDL56rYaaQbW7Wms37a71oW20G/bCQZv1vtgNseAjeKPckIuJ2bUErHlmn7i2sHNTBBWqvDvSWthGM7VWPrFmNrUkUcaPEjKCvMzmNXp7kw2p1VpWb/TVKqcAo1jzURA2onHRY0+s1gf9X953dzEOwZDLgEobPhdn54eLAKZoa+XiMkqspUTAeKjmt+DhDgBxSuJjty3M1bAAa3EN8AmWSMy8+JpwU6XI0Q8tKZi34vANTs91GSQls2V7MvVxD2MyMEuZvePDyzFKXwTQ28GgR4igkyObX4NTvEwIH7iyY2f3x/bozh4fowyCfeIJjjAfUWyIYLfBGmeAQWanffPDZ8FXBHHc1g/I9MRKVWV51UDL4bj6pLvXQQQcHDf2iM5s2GzYoXabfbRvHw4JlCCxVUAtIxcE+3/rBgD60w7QwQrhnKdo34LAZrqgbC3HZusbLR7Ri7fbAOhGbb0YXcrgYUTYQECBNjB6hMM2g34vLBQDr57SmBmRmkBpAVgFyNoxHmA3cQ8CnEOKjEQJ5v7m1pDO7ahZR5Iu+oQD+CDJEC7LjtCVJikiSi8yRSyBto032Af9JUduK9oJ8Xp6vR1jFWetee94iQMN/9Chgm66MmyMYIljRDmirm+w17hHIUHHuAWzcX0ngGBjQuIeRkQMeGs1skjR9g3ryShZsFQI7Ew49+LvOBcoPbS+qnVWtEksayiVUAqA/oHHVjM6u+3YHUxqYg6faNVtUK/ZiU7T+ujLO5tz/mX5gkmfzV7Lhh2MC9zUW4VLM9iuWqPNZOsoqdnubGbnR3sqcfIkB9UIDfUzh+Jqu9vmfTpLANBTuzrBnpQzcYIx3+xCmVWztU6L43B+tLD9GdhiKbvUgrJOqekkSW3QrNtau8HzCxl9RsMls/0E5koNyxptf2wJoE/UQunf/eFqZSl/qf2t1qxBPn4QoHv6qwLGtSg9F0CvAnEBdIH2KHEowT9yFVUWO8S1SKxNWT2OFm2A2Pv5mHvHsAape8u26xs2qPWsX+uzPRuehdK0EfuipzbKE9vLVZWO/w/lFd4NiQ4mAzwpoBUA59kkQP/k4leWYuQiPvkXf8/skbNmeyMBWwBe1HSDeW61zRroLDAUkx6u4GGCOZ2IqQ4mPNYawTnVhQPAU/LudeBNuMBzjS7VR9wmfTMIF/cIuHCMAsBX7vk4BwBzr0bhSwC8N4a+NlBmbMYOlahX99ZweA0SD6eOmP3a760O0L/3FX+1AOiaEQLokUYpYWOpDEPCEma/Udu8w1ZnU4JKrGti0qX8Y6sxJnD1Hd0PIo6UHUGkntBZRc+nKznXyTlLR4JImCxmdGqfLiaUtzfzhTWyBT1ScC93O0Mb9jdt2B7a4cEh74gTO3PZKpFqw2Ruf/i51ZLgrzh21CXtJUkiGTpUiDKFO722RvacNejttrrGFB1wgscvSx3KsK3StcTnIfcBjwOpPXHX9lAzhIov5hxav8X+oOPqSkp95e0j3UMDd3xc92DQoTacLxJbJImNpxhzN7R1ZQOe96kLt/qgLw2qnscX3ALoz+Pg3qxDf/jDH6ZB3P7+Pk3jYB636lcVoHcLiXu5yLGOGq0mWK+NLFspdVdPafUCxjcc2y8uMrswz+3xqerOr80W1qnltlmHAVqd2UYw5y/YGLAm944+Aipvb5PlBORg7/bB4gGgw3U3y+zTOwu7wICL3b3tWDslQG/bzFqQ7KGXcKqWPQToNWT6wRrA+Arp7abV6+jg6xZMBVsHac+ENegPPbYcExxj98+/4oUEscEIcuNzOSccheV6W8EbvpTGgopTKUG1ZNhizsv60aJWyccaa3EEGMKcMuy5HqBjcQfYiOcGY6xXlC7f3ECDKa20QxF7JVZQdfFeH+XOomDx3/mR1QH6v4PEvQLQdXV0lgB2+ES7i4xs+kNXd8mgP7Y7otQd8lOMLQANQB8AOpgChq1MyriDKc3QBHpKUKR3AUClhK3Iv+tOikrsYBdiYw2JO81gnFWOnvN4Z3yDQV9vNe1Yp8sa2FPDATftkD7XcnyymwHQYRoo1hrHm4FBx6brjznkxpBLk2nNKFdHb3LML3RMoLkexgS/a0jeCcXHIsltNJdYtTpvBERV7xwthvA6ztU4hwStqEqgjvAcgdX21sDWBmDQxTLHPQGGfewAHYA+ALoeC6eigvK6GQD9nkMni/6/kQnQdY7+wGWChvPepeKq/ROLEVJElT3CyLFBMInnwNxHAF2u/TGONEPyexUPRRsvr51U8Ie5ig4E3vKNZo1iSBCQsW1jYeAoqSLl2vTwwPqKWm8BUjHraAlX6dHuDNgHH19trXvhyTsNldlgbQ8j0YMWmeg37gqOjEkJdtG19a7qv5E0QIJDjB3mW9sa7Htet3netJ3Z1J7d3yUrD8Af6wzGACUTGNuj/Y4bQSooDYAebSsHbbX82+qgPzGk7CkVIagph4IDcxDdAjB+sySjMeUQAL3dsM2ezJiQ2MNzYTgHsJ4DoGN/a7RodIouEugZvCpA/4vtb2UfcsJzX9uDSY8V+iCHXqpXdB960tQfQ61dGMoVoF77hmuipJBR6O4ctwy3Epux/dzIplSOwNEdpnEbcHe3jg3rPT6Ch6fCwND2MreRAaDPWVo1Iyvvqifuc1izxdaLT4fTPM69wR7tn1786koA/X/6frNHnxJAhxk2Kuam3g8dpmwYTtSfA6wXS4cz3Hg+7HnIYkPuDvAiqxIp3b0unPcJFNRIWDpAnwGgVxK6kbgFA15tC8nuVpEPKXYwN31Dj/MA/77fQLq+tebrqzPrAOgxlpQ+Z2oXd/yw2ft/d3WA/nde8bbrGHQ3iQvFQ6V+HOBbzO/CJij/c1PU3cXMri6m3kJX5rRkbUkkSKUTZVBa1yqmczHQ/j7hjRNrPRNJrJ3W/B7NJzZNZgTo8wQETGKtPLUUAL1Ws35nzdYHh6zb7tt6d6MA6NWkunx9ANAT+/ijNwbQcZ7RzSRiNpiBIjl4x/o63dFhJIp1Dus/1mNf0vkROcdCDea/0UofQaiexDXAPwSeH67uobziOfBgXn7hvilRKhUYQAkzZ8GpDIJKRt5N+I59DrE8EqpzGPBOMOapXZ5M1G7N+6D/p/MXlrpfV8Uht1735Y3ALYD+5Y3Tn/qzFouF9Xo9++QnP2kve9nLbtr7lwD9zdZFb5AwsZDyRXXVXpeCelYsu1EfqYCzRoYb0sBnpgs7O17Y+XlmT0w8+5ql1q+bHYWzc6NOp2O46d7h0vZN1Kwi8+7mbwDkAAlXyZqiHy4cvXM7O0ltlxG8Fr+jrdzWWfuY08gKpk5YMBk8w7USCxzDA5n80IjIK//wfMJTN9+Y5Qnbcfzhgx9bajGKsfvvX3ZXAdDFkIdDNM6h7NeqdjCe5eT6LLm72MKQu0YPajEjxaJ7wKxPizvc8/WvyJ8KcAdTV/TTrPbwLqTZpSSvbMek4Dh6laomFO3sVP+O8wmg3vWfIZX63o8ux8bhnGPsfvpNr2dvaW2C2s5E2tbkVJ2iTELGWE/sT+wC/p2gS0BmCQJqn4OKtsKGCiyKu4t7S7moJWcQUGG/CdDpilyOdBWUVtvQ4Bk4DkGtg/MILuDQDrCy4SZheDze63G8oBoJFpXsvNcVT26IQb/HgkEHMAFIJitSUM9i1ZWD0X3bBEsBCTdAVJbys6AEHSC7C/M3hPRgGeepXd731nBZTpOltRaSMyYZsYlpj8AVP8MMMYB5GCMxyHDp+ontnm0NYZClCHVRaxi6wU9zGICBTVFAqgldCVwiQ+KSj5sB0F+4fbxoqybGvJS8lm/tZR4FK1G2ZQtZeTg6YyRQd4gADTJFscUuYYyEWiUIjvszykSixhEsDMo11todHg99aiNgFuuhGsHow4t6aJj6AMASBKMzQLtNZhm/F6RTAhXPpaoHYCtN7Q+fXq7zQuFE/uJ77EQbZTi5DVxmPZ+Lmc9Rx8g6S6ZlVA+PFj/uc5EhicmkGdRMbQL1ZrNr4wSy85FlSWLpbMp7BeU5XNlqKaXpx4ZtJiFwv2JKSDaK2aSyg+lC1/FIr2n9Zp090TEnd5OajVPUkbvZW95gYzLcM1BzgHXvtz05yXUS7ysVDpznkUqb0Bcjs8tzyG8Xdv+ffHSlfeLV7b9hLTDonOTuU3AdUx616deL3ZVAjCRqXNnyUeumVi4ApVjDtE/ofmIil6yn+NMouFrQAA6e71JgQeeDI3RrTWuhMRtKxKzBXuqtWtPmeQJIb3to6ZbtMxEOZp3J8BxrefRDF0CPnQ0A/dFkNbnxv/r7ZmfPCaDvjiQTh9SeXjIuH48kK0A41yIYs0HeDvba69DxSECucuDAOzwQE7b4hsqop6UIx+be5BJ2rIVc75xxj+U2ALrn34rdJOrh2RfdEwmY2gDeaKFGwzi4u7tRHZ7XVvdJXiXU0UO6/+8/sDpA/9sv/yZrN5S5iJwn5M5MwXrddrCuz9Kgbcp7Eiy64jKZ84aRJtYifVXTRmUiMjw1ivyqT0bNexEGWBMQs7VqDTvR6dmw0RIBlGd2YbJDB/eQtjfzhP0UUG6yQOvT3qZtrx21GpQ4qEXwua9rEd3bPc7KEnvosdUUL688dkRyc9+8hY3VKeeujU2WQd25scGe4kySVZ7Hj+wmg5F41e8UP5TjVzLpjr2L9p9IyOJTSLml13FsfXtU7FgaE4swi8RIxJUwvVTHEewJ6MCEBCZiLhx3vQUlU2IXx2O28Xzo8mVea4B4XPuPnrsF0Iub+c/AP24B9D8DF+FLnQJaqr3vfe+z++6776adZdEH/c0C6Nq2y5pQsS8KflSPq0WBgNIXXtSIw7H9mcnCnhiD6U7tiQnYIzxPAP0IjKkadQaQg1bDTvUlbUcvaCwpal8FB29sBLldRYAFs7l5SvB/bpbZPupdfZE73MptownXc32jFziYK8pPvV6RAJ1yedVWY4HHZ2gDoNMITbl+uLkDoP/BA3+8UuD1Ay+9k0Ezxi6Y8qjfL83hqgy62LpwVY4WHqUgqszux0IuVt6heBXAOPMX+yVNg5ypkwzX5eqx+hfZXGW/i202+qEX4FMbghzJwTQ7c+4bBQA6YCc27fd8fDk2Du8Z8+6n3vR6GnAxheKJBHUEMILySZraU+OpXZnP7enRzC7PFrYPAAImEI2FwhjGXeijFRBDd0jxvd2Y2M/yc8R7sT+yy/81Fh7Mhvwx8ty++aIEIdzR6anAgDe3fr1uQ9TOdtp2DBl1APWu6mdDVh/XFzJ4fN0UgA6JpysuwC7wM3gQWaoC3Ik9S+kFkSWpZehXzhpltVoDQOeVrtdpAHdpbybfiQRGd2abHaknULcugKQa9GAGpigx8cA31CN4Ypgsndjq2tag5SihZvN6yxa1ps1gwOWqFwbNDLKeX4D+gu1jXCPwVQXosbbEKRT3nLMZUhFJjRCyQ66X3h9XPXHFpLO8JVoTOtUWMuQI5rxcX8er1ez4oE8X+PV2h0EUWvMgQSUTIK2POAeAV8wpGHWO5nM6yENGDode1ERC/o52cVjzcFUPAHRPuH70mdUA+n/1shfbqW6djHmH3gW57U0guc/pAQIVU/Cp+Aw4TypcamClxYDVYbxUBzhvW7vVpdJgL5lags87HgmgexlFXksI9I8M21RvYIzxBJhJaZ6hXKJmezP0V4e5Hwwj6zITzdDms2b7Kb5zG2F+mgA6FDcqjUHSKWJw1YLXa244RYAO5lhmn9fgMp0k9kcPfWKlfeJVBOi0ZSoFv4V5VrDjAcUdkFfgfAGHPIlahUdVQbzgNWexQ6hyTcNvkarWl8AMuHTMwTkhevRRxjUQGwiQju9OrWmdWsvSPOUzA6Cjgh390tnP3hMmUQWPXu2RPkbpw2Ppb680dj/yD8zOPiuAfmUXpQ5mWUPd3CgJB5ngZe8E6ADwFed0JP+4PlUBuq/tAarZ2gx7RFO17ATJjqWijMDzk0wQVBORANrErY5KA4Jhf+F306yJaaWmMjTU7LUlbZ/LoJxgHRVakOkHQEcdPQD6L/7m6gD9u+77JmtDs1+cnuqRNQu0RshcES3OdtniLIza1K3EO5Y4oaHEmOanvjyZ5j/F7wvDwHga3y5K7lBmBfVkw870hrbeaNs8UweNC2MA9JGl6cyydG7NXInlSMgPuhu2OTyswaqj9EUqJ8Z0zpz7DmJos/bw4//PSnPuVcclca9+RRvVe7a3yZzfvrZmW91u4QeifHKMj/yHAHSr4ByfOcrj4proSnjc7bEeGG3sQQVAZ4atHMyIGxUXqke7jIR1oeOZu3OY6sLHZ07vHpAG2GeQ1D3U6XCPuTSZEKA/ePmy16GLNPujZ88vNXYHBuvWDzd9BG4B9Js+pDfvgD/zMz9jv/Irv2Lvfe97aRJ3M76KdldvebN1mgLo6t1ZZvYIYEIGXKnXhewPJmEXZ4ldRFA/S+3cLLWrs8SenS5YA4ctG+ziWgNMtxzcsQkOUbPqNbtYXACQ6UzOvuFw1EVf2zoZkHles5k7rEI+CdnuoXbD1ry+mBtrs0m37nAVpawXAW3BoIO9UYYRrr9YwSjlYes3sEqJfeZzH19qMYqx+0cvvbNw3JSEyM1yvog9D6mp5GFhkhIBzEEZVJEo9YRIhFM6RrHbFit8yNWR7dcWiUexV9GGqdwEcIQAqTFmus4h3/X6Rgf4lDE7s0WzMAf/GMMf+NRytfs45Ri7H3/jV3ED4mcvmB5tLfuot80yOzdVH3SAczzupxlVFZh/kFzKfC+UCpLFR6/R+DxVgB6MJd5FY4C6cQ81fH4DIIQRGAYz5r84ejxfj+vof91q2uFO2070utbHz+126SFQqtaq4SDfDAYv3/lbq8nvvu1N9xhM4tAbmq3yImUf8RK2cg8Uot5eQYF+TybN+8ezLt7r6TEH5vPE9vYntlikNpnOrdMwOzrA5q/XI9BFiyzUm4/JnkoaT9l7MM5gVN1/AUN7ZNi0jV7D2qgRbjdtjoCs0XG/CDfdKwC/LobHWvpHuOffhBr0O7cA0LUG4H7FKce9giRftLnBm6pzheTqUfcNsFwNrIKIAgjFazmfCxMlzbEinK2AK5ps5bldno4JkF68veXtesSgX/X2PGqho2SCjIY0PpCGX51OCOjRWnGj07ETwwGlilenkC/LIZ+sOVkR3f/49x8/8/hKa907X/lSMuhUIYGVT1M7tzO10TyxyWxBdh6JT7wZZZ+tpm33O7bZRQAOfxGuPGS+rdGiG1cdQL7ZtOl8Zld3d4h0UHPKxGAD3QTAcjfJeMO1HfMwr8tgaZE2KUu/OFpQur7WBHOPtwAL3rAnJzAaRaJXJVhICM2yGltw0kDUW3FFMkbGf5LoxiNuDoyh+tEndvbscoqhWOu+svVfWxOAwpUNAWSqYEfs93PXnxcsOs+vdIIvgbqv+wVQCpgSwTp+Dj1NWaM6zxdMVLN7gGu6qs9U6Q7UamzAxlagXXZdp+2cjfOFXc1nVBkIqEs5ppkaUF3eBGfT1dY7APTHnzS7tmf22NNm46nZYK2UtOMjg4lmsxhnGQB+yZxjbYpvX2N4b4C1VjMAGYHV0YVCQLo/8OnZFyZiGzYvicKcQW04HgH8cZzJpKxjx/KA9RTXBcejWV1LbDivm7wM+Xc0PMCxQ2bP8/D2bFjbwbQfO2z2f77vBgD6y9/KzjUxGxjjhfrA22Gen45sN5nb+Qlam07VtgtGYp6Qx/oUysksk6NOKDOibV+Z7tc7BUCP9YoJDW+BClA9aHbY9vZUd2DDZougfJzMbHc+sUkCy2FlUxhHIkbD2oUESqNl7WbPWo2WdVodPvbbnlFxBaPWZ6x7iX328f93pbXuDadPFIZtnCPsGtHjHg8GHcB82GpTuQmndhjCQfGEWm5+5tzoG7I/nxfsOl6LmvUoiVLnDTeiK9Jqce4axzAZrfpM4PdVvyN8ViZEkQh1o8aI/dSPfW5P74/sczvXZIaapQTpx3p9Xlew6Pi+MJ7I/M9byn7oyWeXGrubgUluHeNLj8AtgP5neHagvdqjjz5qkLvffvvtX2QK94lPfGLps78eoKv2JJjayqZUABZfNNjvXID6HFrmTBO7Ms/Y93xnnthFFIjlWOATry0UQ4r6V7ZAcbcVBHMA3Mieqs5S9S+oTUzzuo0ytM+SKI/AK9NudrgDpkmGSFzAwIh5tlMZxJxtrrCow1meIl+aiEFyrKwo3o9SVAS+SWKfe2w1d97/7t47uJDLUV2Lq+TsrkZw8BhBuwzkSka8YMZ9UY8QvNhQr2POCQ8CpFeQQiin2BaYbqDhVi4AF7WKcdzI6mohd1dqd5MPFj4YeLLFBOgC8B1/xEb+Tz61eh/0f/N1AugxHrEh4RFzC0E9pKUA6+iBPgI7iD7pAGqZ5p8MazTWUizoZw6Nn2cA9FJdoHAimJEImFjXSZWFS/0RTPEYYqGDMUfAChi23W6zbdrRbsdO9HustR0gwgulSQGnqrem3huGVd/5W7+z1AYY92sAdDCFwaCX1sT+2d06GIwNAy0YunmtuJITUX/vDLuXGcxnCxvtj9kvezyGJM7s2EDqE0rqwCZOoToxG82w2eNb0BxdAri5oxbdnWvx+0PDpq1369brdazTbRGcA6Szk3MA80r5hd8KHuU/PwA95IF4r6KFDRJ9VFvonkGwxf7crP9WO0Ml1ypX2A30IpiVMVtZsiKX4wjxCnGz1gv0/R3vExy9ZHvLttFPt911gD6zMdx1K3WDem+BnmvTqV0mQG/bkV7PNrtdu204JCNydTotjPeUdI11G+3KMvvoigD97V/5UjvahpklJO3yPDi3MyFAhwcCjIew7uIckTCA58jxYZft0iSTVZkSpen1Jo3Y0Ce90+7YZD61K7u7RC31FAUQ8kagKqqpOvvDvbYSRbAfhmlc2rJFVrML+wu2nRw0YV6aU0YPTfETE7MLM7ML84wAHU7tAOnK+Pi6iE6DNFWSMkJJu9LsCu+NL7bOzBI7v2If9Fe0vsWadUncVThUspAxP+K9qzBd61QJ2pVwDTl+KTQOxUw8lilebRJk6Ny0MbqYcP8j1PZeyr5uRdkUVlNcBzgI4MwhKAZQ7+O3XA+ggElsN5/bNE9tN1+QnYUqrSSU2dzNsjyxp24EoJ81u7ors7j9idnaIxrWTAAAIABJREFUuhmq8oJYXKA23bGR548K1jxc1D03Wdwb0f8cIwRpO8zbsHwD/GMKtYaaKgD/TOQ5wB4CwLtDPBIAI8juIVV3Nh/HYheMthl6ohOkI2fka24EB3y+s+945LV2j48A6MePmv3UDfRB/66Xf7N7s5QqPrpEgJzwpNkzk30C80uzqaHePBR+VIBB+eLO/Fx7ANAB4iqJylAUadPV5wixecFBU9olJSOSY/1ml+0VT3Z6Nqg37fJsz0bJTL3PkaBjgtmjIXYMUGKBbXPrTQLzLkB+s2Nr3UFR2hE7rRJ4C3twRYD+dWdOFsrISOYf7w9s0Grb7esbXN8CFF+bzVl2g1Ki2CeQ1AA4h7yc9zeIqZbWahBVkJfj9QDt8gvRSBXMeFEQqU90ULWghK2IiYMMetndQ/s+lHo4t6f29+yzV68y0YxSHQF01XIs2EY5M3RoEkBHN6TM7j+7XOvhapRz6983fwRuAfSbP6Y37Yg/9EM/9J891g/+4A8u/V6FxP0tb+FiyVpQD+YiC6y6Xa28YTBB5mcm0PTEeG5PjheUuUOiDlM5LAjq4+surk6HFa2s2H4FAY+CuTRTr1a2DONq1CEDkscjlns+b8pjvnBjw7hYtjvWbbftwnRhF2dYIFPW2rDOFjJIlwsBwNcaHbLsPWjIaDDmRUKUUiX25NnlzM5i7L73xbeTOQsmN4B4hMTcYhxkx2Iqduu5ZO4lJuGy68/ThueJB3+sKsfwqgAVIUcPgF4y6KUoLRZ8uQUHUCvZc9Wil72zaRYH9sRrsNFOBM/BVfyHH39kKZCJ946x+zdvfD1dXau1VJF5V/94JFAEvOFJAMUGzAcBQsZpyqw1kjqQwoPhmtJQS9LqUE9ga6OLs/eaL9oZeWu3AKv4vEhAYN5Twu9ydrEFsulFnTkUIYc6bdtkexUAdGTRUQfsCo6iHi1ux4NXKvismwXQxfgqkYAvsJhQj+ztTW02g0RYwLzfb1uv27Q6Wp15I3hV8QpYNy2zVp7YbLawnd0JwdZsnrqbuVD0PBGLC3djPAKkE+B6fqzfkhHftXFGJ3jUAeN6bK13bNhr2/pa1wb9jqX1hiWFgZnG6fr2akUOKsAlAkVc62iz9vCTS827mHMvPnySl6CqWAm/BiVy5FKPv0dPcjEjXvVXyD5DBROdDhA4yk09vjCHKMsmc60VIT4X1Qh5ToAOZ/LD/Q5lh5udHmXrAOcImiKRWCQFPLkH6SIYdBwf5nCQt58YDLh+gzUp6vrdcDJmIdbZT55bbexeededtob2VlhvaF2tQl1I3eHAjsTdlEA9tfVO0watup1e79qJYbuQ0crAsWVZrWFZDaku1XvjdVdHI5ZhwJoMaw5ej89zZaZ6TJi7YYx7Hd1r47Rt87xuFzHfEshBU2vUATrRT75mX5hkdnGWGzqM7LssGfNRXRgCqHtSz50PZcYUCToZ1WG+MImYpXbuyQdXmnevAIMOiTuPHe3vDm7ZpRFc2XbtIEN+EKgX63e4ubvCp3rUKK3RPoTabcmbyZszMYHUhH5XPFdZ7gqkh4OLbDsDsMOLGzXEcH6H6zsc3Uf5zEb53M5lewTqSo7rWADo57L7Vxq7H/n7Zl94Wm3WPudu7pCZAyOSkb7ufmZSyqXuTAC6n871AB016szVoNShbba9LnYcAkXk/LOmzj+Ydia/62Z9tHOrq0Uaat339uQqz1siM1sbmPW6Mq4D6MfreT5uVMdyK4l4yiy+s+p4D75nbtbpmB07ZPZv//3qDPp3AqCztayTEoZuOQuC88uzCUtlrs7xOGdCEsAspOKYMAVIZtYUAD2hAiC6kRTW9gcnHX/CXGeteb1ph7prJFQuzAHAMwJrML7wEcKc2pmNyJxLMQRyp1R+sCrRE8ThGB/JpkGnb0eH21QJDFuoBy9rsdGy7cOPrjbn3nT7cV8jtF4AQN+1uWUb7a5tdgGym678gydLwuQj4k+MIdZffF+ZTlnfHfcA1mkw6FBqIbEKldGZtTWW8WDtp8LJ3e3DzygIlUjnBRGjvQnLr1z1lVRBnKRziSTLDIRGsrArMOMcjXh++Bn7VJEc8IMGWaT9KrMPnr3FoC8Nqp7HF9wC6M/j4P5ZPHTRB/0vv9na3gpHAB2umloSCNQZoChjh2ASwSak7VfmAuhnRwvKjlHrtwDblAigY1PW4XyJ8rpMAHOy3OgFSamnsraob8MXHNcRyNUbPfW5xHaP56Vghhb24u1tOzVcs/Vuz4adrj05mtlTY2RfE8qNchhspDKowetqdQCTLqXwfWQN4c4LCJpn1iBAX9hTKwZe737RGa/9QZAjuW5h/uZxTrDiBSinYVNZix5/F7gPkK5wKQxcvPq/ADLBigTYDofxkOh+MUCv1o355lfUQB6Ut0f/bxqisTZPNekB0NHPG8fHuX7/xx5eKuiqAvT//Y2vLyTuUQ/Myqsw2ot2a6x3F3sJcA435nGKlkkJWXSAdmz641Rt+maoUc+N9eocn6JXqwe40ot5ZhvgVGMjd/MaATrZAyY+cqtR1pexzrxXr9uxbocqDsjZ8U1Xd7Yc1LW7Xo4WoYbWgJvDoD8Jibu7uFcB+mLhTtdXx2TAYbIFU6zBsGP9Xkv9UVtIfin0Z60+1QGJtbI5Qf21PXQ2ECDn/YgZDSY5kSv8Io3OC/pEqGFHYAnDLRzv0n5qkwWuldqtbW/0bNgHQO/ZYNChSza6KxRLQ8VAMcZQS4bmQqg9bgZAv/fIbQVAjzW56pSrtS4k+hLpRmsz+UZI/YI1MH5GoAk25LkAOsA2Ax4C/NL9GeOE+XxxPLJ5lqiPbqPuAL3FhADeo6q2iSQpfrc3n9nOfMYgFwEeAr7jg4E7yMtpGa+X07Jk0fhCYuWBC08tdc/GPvHC02es36iUeeRm3Rz1ymZHsLbW6zaZoxVaYhsd1OPX7faNjp0ctlVHT3YGtaNIB8HZG99g9WtsO3htPMVvrQ/lFZy5Ww3e98+OEbijzlLlCP1ui/f0fgpmvmaXJppr7QYC+5ymcLj/z04yuzTPbJTBjDC2ISUbC7MmJBrZNUTqjzBZY70sfRckQaUyKkvs2bM3AtDVZk0AXZy2z3JfGaoC+LKzRjDsAc/VXk2JoBLUlwL28sgV0O2spgC6Pi/dnp3rVAK4fH4A9LJrNtCnDACxw8FMrl1r2KDWsUP1Ne7d03xu+/nMns6u0R5u4olpHBqxwPns91aadwDoTwCgQ+ION/exwDGAry/lkkNVFEsHZO249h6GcH3xmvQA6DmM4dyQDcD68CEB9JDqhz9GAHRK3Gtm+/uqdd8fCaB7Htc21lTHDrd2hBv4Gxj2MKvjmis7hULuXigg3AcsQ6K4Y3b0kNmP/9LqAP077nur2o86QMdcv7qYcR89P923/WTOtWSWqqecvIN8kFxREYURwaCzh7abj8a9Eet17HGxZnchRQdT3t+i4eBT07FNspTGdbjXUM6CgdvzfuiRHAjiwNNovlR7+09PYOD6rHX6dmL9qHUbbdvoiEnHeofrjZZtv/e5311pzr3pzNESoHt/8xdsbttGp0tiCGVIWF+5driHEvxC8A1JOdhzKJzOj9ASViWXKPlBIhUAHeAcLDzIJjDq8BHRNVI5QYDlKmguQLoDeZZoOkCP7h5skeemoBhL7F1g9mEWh/OBbxB+JuGC7jeM61yjU+ES8Jk+ePbcUmMX++mtx+dnBG4B9OdnXG/qUdEH/aGHHuIx7733XnvlK1+58vEj8PrXX/tG63uPEtW2OKPlbsQI6nDjI1sH5g+LO8A5GHQEQPhGthzBFEA3pOg0F8EuyXplbRBY2BnUOlBHNlatoGDFE3l8PFOOKo1G1+pg0tG6BzKcFPWaC7trY8uODYZ2cm3TjgzW7Jnxwp4dL+zqdG4XJzO+f5bOfHvJrNFoWqvVs3arZWv9Po1FsAOjZ3UzndNB+MHHVjP/+W9edKZwzwxZn+PCgjmPBEV10a2y6SXL7vW4EUwEp1GR1OJ1EYpo81IVWBiOItOLTTOY9CKAqViMxObJa1JpGxXGcJR4u2kOF/ACoKvOFmwxsuFgSb7nI8vVZOI9Y9796Nd+FY8VmzoDOS8RCOaQsvVIaMRMdxZTJQqqR4eZ4D6Z9cyuzhesVT8/czOpaCHnTqgaI08+eW0hJYnR593bFIHNw1a/3Wqyd/LRToc15+utBsE6xkJtVVBCUQklgp3zELr6+QKgQ3p2IxL3KkBn7aIjsPFkzjry8xf3bH8fAE5meZ1u0zrtpvUHbRv0W9bttAjYcU/CiEfSdgBztaLC2KLOnK7gUzGY7C0PgNRDUkIRMcZnUIMk2QFlltulcWZjAHSYx+Vmpw717fB6x5ooS2k2LIFrL4NGRQTl/A+QEGCqAtABDcBSjKaGJMRnVmTQ7zt+Wu/JD6Qro7HDvac0WEEg+t/BQoBFRxAEOaDY1Irvg7c6C3ZHQM+TWs6gq3RIGRwBdbHj40WAT7H3vaZ6eav1l59T3PJkCr00xwOvkE9CiQJGJOr1eW+443LMDUKrLLXHry7X3zbu19OnbldJBVUlwkSDPDNYUB2BSgP3QCIJ7KEeeo3X7ba1Nl3YEdKDqkxrTUvqTc4NGBLOFontj+fcW8Ds9Jpmx3qQ0deYWJommX0BahCUvKQwJ61Zq92xvNawy0mTXQAuz5CUU0cCgCisBwCge3Bwz2oE6wjmw3+BAB0X3q87koLYu8KPg+PuPh69dkO16hj7LLUnv7Ca0uorW39dDLpL1MukXfDW8ZtS6VTy5TxZZ9/93762FG3WiiggTOD0vGLJDMIWnRwoF1bCiI/eN1mEbtwDzsJVZO8B6FkOQB+OjKqJoTVYmz6gsR6AeWowj7uUTbhHzNAmK0/sUraao/YPf7/ZU+fM9sf6nidmV66pJzoAMthp1poXjH2ljZrf6wWTHj+zpaTqyGE6h6kLGTpw0ua6Xh+dJQimK/J0drjB67wXeyiI8Hrc7mDjWzCGa4gpDwNNOspXzOTwXNyyTIK7YXjNhSl4GpIFJ4+Z/eSvrA7Q/8q9byaDHbMBa87TkxGB+WWy1oiV0KpWJqKq+ZbdaqwtBcFCKYJMz1Dudf3cKtby2NExpoi96g071lunOee52ZTJ9F6zoxgimdAMbp7MWDOuNVLJMu4vnqCNWRm7LFLmSBdtdtfsjs0T1mm0bdAeFr3YsU7uLab2wUdXSwp91YmtqJos+rxv9gZMNkBej25BMQRxT6+12ixxY+yS5XZlis4zE0rHAY4jkSsz0VbBoKM0bq2jFpmFWtUd2kE6Yd9Bwlbyc93VAeTV6UNlWADVZPFBfHkCWRuOXiv/FNFBESPGGlNcTF8Wse//3tPL7RPVY9z6980fgVsA/eaP6U074lNPPWXf9m3fZuiDvrm5yeNeu3bNXve619kv/dIv2alTp5Z+rwi8/uVXvcEGbQUPYjC9dQ6M2xLd+DC8QDYO7X8gKb62SG13kdpOYraTqO6XNdgA0xnMYpBZxEIASS2ObVZnJlsAHaEAGIkA6NH2Rc9ANhFOux3Vl7NAK7N5MrY0X7AG6EhvaGc2tuz4cMPOT5ANRh/HmZ0fA6DPybZrUQdAb1m73bMOXJL7fS6ulFqizpEAfWH/6dHVTOL+9j1nilqlYCGKZdCz0Qr4y8SHAGfZL73I7jNwr4AWZxC18ZUGdNxsC+mvNtKivpoB9BfXoEuuptU3NrnCIM5lsACuMPELphz/DoAOwA+GTwBdvZoBjt/1+8sxSjiFEqC/jsfiRuwgmpubS6wwLticlMzQuET/7pjsZMxzIzDfS1PbW6R2cTa3nSRhZwGlffSZo4816lmpDnEXd3xOvD8ABhMbkNg5qwwwcrwrYH6i16Gkvd8Ayy7wxGvjDtUR8BfRRRnmVFxv9TluFkCHfF2Gb6Je9kczAvRnz+/a3u5E44U7ikZbDVsbtm2I737b1oYdayBJhcTbZG7XdpAAK5MkAOj43kHDdQMwgqFX3TbXemqh5QB9aDMGd5M5XMcF0INBR3B6x5G+HdvoKHpt1AnQE2/9g+vwZQN01M+Nbwygf+WJM0WniiII9HtCc00zK5JYeE5IFxH4UOruCSKxobrXmPRxWWd5/5US94MAXcZtBNEoSQjHbbIZYOLlyCu5fSWbUAHoSH7S/M1VNlHHSHM1tpoUsxP3VpSRYO19aufyUsxI3K+33Qa/DQ+cXVY9pCQ9t8NQUCAZlOUM3g/3werX7eRa246gUXkNrLfc++f1Jtv57U3mNJeDKSHq8dHCi7L4oRzgAW4mSWaP7075iO4BCMxrzS4VGBcWAuhXFgLoWJ8E0GVIN6eZmdpuam0WsGRZRymh8bIYuekzeYsx9Xuq35Yygi3estSeuGGAzkzXc+zVlYyRVhVvIHUQsIejQczRcG1XiYv2O/0twHm5nyg5FBBcj6ytJ1DX/iTAjt95GVpFiR3PQ5JKr0gMqZeeZdarNW2rLgtyQHfUpF/IR/R4maDver6wS9lqjtrog/70s2ajqQAuAPnla+pvPpmJSQf4rdaas7WZzL11j7ihWyTIsC7hNQDpY7DfAIIo12maraH2HJ/QJfAA3pEAx/MCmEPejp8heQcgB+ONR3zH3hI15fLkUHeBIKjRwAT16ZjnSC6xBBBcBOYrEsZds9uOmf30+1cH6F//om8goIz5gjXn7HiPjOrV2YgdERqUrWdWh/8DQLonYHQ/lLGLGHbfGwthpK/fPt0iPcTZTCl/nQD9MCTu9bpdnM+YDOu3ANAbNp/vW5rOLUEcFgDdZxHXLiaCYmaHJR3uZwH0re66vWDrNjrV91pi0Nk1giqjif3O5//jSmvdXzyGhII+lOIlqGk61kDbMygywdL7XqEWZ3U70uvTPC4StfACgUP65enUnoHcwo+D+AOJWLDm8A0JgB5Set39KidDazSw8ojDvzRAR1ti7L1KJGOPEjB37xwvgQxVZTk3fW0ocy3FOWJv+dCzy+0Tz7Go3frVTRyBWwD9Jg7mzT7Um9/8ZgLyn/u5n7MXvehFPPzDDz9s73znO219fd0+8IEPLP2WEXi986VfQfk37lMGjC7hpJGIM0dcALKMkho8Apzpu06ndTkG11ijBDkb2t6gPhiLWavZE8vrgDXMjiSBR4sc1JbTMkqbqQObJmVQYGsVxk+TsSXZwjY7cMxu29Eh+lAObJLWbJrWGMDtzhO67S7AoiMLDu6G56BeunAEFrslBqjFREFqDzz26ZUW8nfefVpy72o5WWTyYwUPIFepRQ82vWyz5hsz2yOVju8FCHSAXvItWlXD6Cwk7lGSELJd32MKUB616uHySXm2O9xjM0Ugyn7fziwDPqMlngz2UE8GV+UWrwkc8N/+oeXGDecc8+4n3vh61tvGJsigKBIZBeMTplwuHy9mufoqQ86OtmtweL8CmexiYU9P0MtVBnMYpdgwmXhwp23VmAvceiWsp4XgWCwFwpFOywbNhh3ttm0NLHqjQWAeoDdOBecRkvEA6fq5LESIgDkeb7QGHQw6cHPTAbpqaiGlxL2X2bkLu7a7N7Ua6SXc0ypJAMhuQQHQb9rGWpuGX2g9qJpz3Lt4On7ObYLe1rjzYJ7WbNjmeo/y+B6YdwBSv59bMBaCOdnOxKYw9ZsqSGALpNzsts2WbQ+a1uq0yaIvmh1L2MO2vPJxH0QAXUgmC8DsNejjqSU3wKC/8sQZgZIiYVWCGaxr+L3aCwZlpiQRADGNzoJJd/CrxI+bixXtCjF/BLLFaov9LxMRnthBsF8xn2NpkQPzQmpdke1itMr6aWfTizIYrQxxPjG08TPGk3WLWbYyg37q5BkHsM42Q2Kap9aGKVy+sG6e23oddaU1W+vUrNeq26FB2zb7basj2drs2swaNrWm7U6mdnlvbLUktQbq1pGAzRLWmZ8Yoqe8Xg8m59IEvhMoq0IganZuhk4fNbuY1m2W121GBwWw7mqnqLUY4mx5jSi5GQoGb5Pn1w33eay3YXYloJoTlKG1ILt/NBD0p/bI48sphmKtUw06+kOUQX8w3FVQo3sirmXZBqJIqsY1dpk7+pNzP2PKQWfOa+7wHn/He4r35ArLowOI4z+4sANwg/Gm6s3fO/4V54tO6ABU+DvLkfQvvid+q3dg+sQl/Ej2tvk+o3xqi2xun09XM8X8F99n9jjM4UZuBAq1Huq6K7XlUwfqANz4fTDYYdwGEI+/4bZlPTjXOYF0eNpieNirHI9Nd2efacTQjo/eJLDGqZmNIa/3Vm44Hozl8FrY2+B9h12x6B0AcCQKvINGbMKYV9FBAIx9XFtMXJrG+efD+x07YvZ/3ACD/oZ73sQ1Besa2HIoVfa8Zno0x3VJLEumBMe1bGE1guToW1dtQ+KpVCpnFDMw+qg3XXkiWTliLiYwnamtsywCMRgtWFnehBnSbbZ5jNl8j+A8BYvPGgGtk5zLDjDxTlFaFFhSAL1OBv3OzZOGWLFJxaXWXRwBJnH3P7ZaUug1RzeceKmgV6g5i04Ibp6Xo7RG5qJwdz89XGN9OrxBSoO2fXvk6lV6M8E0Tt2E9BzI3AHwwagjPkWpEhIj9GTKcrs8mXAvhVcA7ruozQ8FJvcmJ9RKdaV8UcI7KGLCeG3NTZqLUCpIocpHxXt/7NK1pWLipQHIrRcsNQK3APpSw/Wn++Rer2cf+chHDG7u1a+Pf/zj9tVf/dU2xq6x5FcED9/8gruZGcTGgIAx2O8A6Gw1lKVFX0eynHDyJWkHsx9kEx2go9dyprZEYD3gqNtGZhMLu4OvMCKBXJ0LUTJxntOz+A5w0VIDAL0JOTocYwnQUVfbItO03VuzjW5fnto52k5hE5LZHDYe9DFFEhzOn8gisw0STY6ir7t6n6J+7rNfeGipxSjG7h1VgF51bw8AEEy3B9IMkRwgMBQ7UKfnZnPVGvUI16I2PRBMwbppg0dWtGD9XJ6p4FyTogTykkpGoM/2UF5vSVBRBej0IlDtOTcRAPRG3QZtADQB9L/5e3+y1LjhXGLsfvJNX+190MuwVDLFkk0qNuQqcOdzJMKDm/sImeYksYvzxK7O5+ydjtp0lGDgi/2YvbZcm6N6kYaUn6ZXXleHf8MMDn87M+jZRrtph+GuDZDq7eaCiQsMx3MJWbv3ZI+fEXxHnSjO5fkC6MEGZ+hJneV2/qIAOty2UcKB4HSR5Gwxhe+1QdM2hpBTm3XRU9jrk/FaAHRk7UezzGow/+p3rI1x2BoS3Lc66GXtdsOY5zOViVy8vG/j6cKms4SvZxui3Oz4WsM2ew3r9tvW7rRt3uoSpFehynObxJWaVap7Kgz6QytK3AHQA4iHsV4sm2G4g7UlEh74W9QEslUYDQkF1KOGOCSOUWqA10ZfWjj34nOGkU+YIIZHFI2ZvF8uxkpKmGiNWI5Q3AdipKL+2NsL+nqCdY/3uisCouViYSDorPtjVy4sdc8WEveTZwolC64/zmI9A4ua2Xo6t06e2hZM65DMa9Wt06zZBpQavbbVW12rNXsE6OO8YTvjiV3Y2bNOltkAY2Bog5Zar1mz40O0UqrZsK09aW+OMhazZyeo78zt0X20WzS7TIBesxxqDK895ciEDJRgXcwyPU/cgCnuFYw1Eo78AoPqnhxSGyC5DNmzmHysfUjkPvj4avvEy1vfYi30WD8gPHe23u+EwmdEuoniq+TCxYgRBDsob6FjOT4HgbgAOtU/lPFjHZMyTklvvNYBOmTnltsuW6SlNkZHdHfLZiLJ0xyht2rXWta2lsNy6ZJYn+5gHd3RZ/lUexEl72DU1znuo3xsi3xuDyS/tdK8++ffa/b5x9UHXZ1SShFCmMSNJwLgYLcBxsFMAywD8OJWBdsdcniAa7LUzqLjNRg0MNqIaYLtBmOP57FMqGE2hH0Nas/h2o71dOH9z5GDwzrqAH0d5nAdeHIgwaO9GccARg1mn9L2hs4RHygk+DxfyOcXas129LDZT9yAi/vr7/56MteIuXYJyFOWfNDPJQFznTD+SlELjjp0qhrlDRRO/dW4gh1PkNBHFAXz3XqLPyNGY2zjHh6Iqah7y+AvBEJHaxOk6Ej0lABdDDreXyWPkUjTLGTc6LOxSB75/g8VDQD67RtwXG9avQHvIiSGVCKE+/U/ruji/uqjm4VaSCqM6NKDsVOcFi0w4T+CPeNF29t25/omTTv7zRal5iiPemJ31z59+ZLtzOZ2bjRSLNKMrkIC92DUUQsOEznczwLomV2coLwnofkxrmEoOZiy8OtYjSHlG6T9B8eLn7W2RYN0jW1ZLVmugbHyIOb/5JXdpe7XJeHHracvOQK3APqSA/an+fR77rnHfv7nf95e85rXHHjbP/7jP7Zv//ZvZwu2Zb8i8Pra06e5w2DLDWOicDBGMMp61ACVnu5F0EJQSBO3BusDZ6wFQxCLuhlILFMz1J9j4WQpjNxafHlwEzmYv8G6JhxDleNnIEPHX5gguRwqQ49OGFxoAe412zS3iwAmWHyIsJg0QGBCl2tJkFRXrQCGvXyZ6UVCIbWnzz+91GIUY/f2u08VhmnVGnQGEQHSPQIs5HYVgF69ZqoFjADBq644FGXAdv2/olYsgs7Ixmshj1ZrB4P2YM+L+nMHsADoNLBxBhm1pQhgUZsNkD6ATBoqhI42ZbTT+Y7f/dRS44ZPE2P3c9/wNdYDPVGJRyNIrYancmxVPTPkcSid2IfkNUH7pIVdmi9sJ4EfQmITBCIJQI+CObb1c+l+HxJ9/ixwwaAJQYM7tm82G3zuoXaTsrPtTsu6AKhwQg9n+woAUuBSxPeqGQ5ZuxvVqI64TMTQl4HJptTe9durGdigzRoZ9FTySDlDqwiAATWcr6+NbDye2Ww8s/lsLmlofINJB4Bq1w3O6+udUhId0k8AsGZbpnLD9R7l8QMwoWDOIxUf7Da7JmS2szNmLfv+/sTms4Str7AuoP58bdCywbDWRJ7BAAAgAElEQVRrfbq4N1mLXKpBSilqKTrxg8d70Jk5JO6prQrQ/8JRmcQJKAnJRXIASQUErmG0GPdrqFMIYLzeb8I5piAqjkXQ5PcSZJ0IktaQzPLyjZAUY77HMeXWi+4EqjWMBFUAdUneVU7hRuPFfU0JvI+Pagy9qMfjsEItU3gxyxfk8zcA0EsjTLE5tzXM1upmt7fM1hso9hZNKT1UzWowWeN3l99gvkdpzWazmY3GY1urmR2lAgly/4xs5bDTsG6rbocx39hGs8a95bERkm6ZfepqYjuL3PbzuiX4O3sJY42SN0Z8iQuMxaU03IsVlclHv/e1BsAoMgwk4Z8CpUDG+bDWrFN98KFHV2PQX9p8mzXci+Vgtbnu2tgTdW5la8GyDt1LKNiXXN4XAEmtWpP7oYC6/oa1ALgPrwBQxvGhGtH9Vtae418C6ImN8oXN3dm9PJdc6ioeHwnK0n3eC7YkxUVC3HCMCaXxsUcPax2pS7h2L+yTi/+w1F4R+8Q//R6zzzxstrvvrDkiDpewB+sN0ziYsYFJB3AGMw0QHH3QXUikEh6XLDB1411CmMx2R3g80mQRcvOQOHufdKwXwaDjfcjiYw1xlhz7AUA5gHe0VyNAx7fL3/GIv0UNukplXP3i54fcMgA6+qD/2xsA6G+4++vI8LIm2wE6ktday6Csctoe0na3oYeSBd+clw74MAfI1Ho9M+I7gPIuPTPgfyF5gVeRWwKiBvMiUV911kVz3VMJz6HOwHowkLOUZVZzgvTErs7Gtp/MPIZUrIbvaLWruQllQpuu7eudod22fpT3FlQ6/Lsvivh8939+tRr0r7ntiJv/FttF2Tqw8BHRXaDYsmYnB2t2tN/neIMdB9lFM77RyB7b2aFMHSbGjENRw+7JX8VpOgbGUgldxd1g3THupCQwt7yMR8ta6WkSa5qIGCXwgpSJ+LgMtGLn1ez2w/sMDJIst4evjZa6X5fFH7eev9wI3ALoy43Xn+qz3//+99sP//AP24//+I/bq171Kr43DOO+7/u+z/7xP/7H9ra3vW3p84kN8DUnjkuGGWYxBDfKV5JtqmQPVcciJhrBlLZvyG1zG88FyhFoYYEhUw5ZXL1N3WFG1knhLKvl/D1gUiIKQ8tMGVRpx+w0elyAU4YQMMBQEINMLKWgCKQQDnKRg06taXldRnPYdVlvzCAZ3sEQRuVkfXCMKZj2LLUrV5art6kCdLDJzMhXZdkE6PFpyzoufW6xciUzoqBQv9ffGFB5rXoBJoorXGjoi2PE2MXoBgun3t4C6tXa2DAkCZDOgK+QtkMuKuCKABbAFo6fQziAo36qC6BWs3GW2Xd88JNLL+Ixdr/wl7/Ga9AjDCoK28pP6qBXWX/1R4cJ1BWw5bOFXZjN7ZnplKD80gIqD7QS8h7Hrg5AbTkSDUOvJYVbOzd+r0eFfBWfE1J2fFbUnPebCDpUC6uWgQ6AowVT9FGuXBOfhQLq3vc1jL4Q3GOjRcIL1x212t/9O6u1gKkCdCXJpAphfa2zq3t7Y5tO5zbam9iULtgunYQ7e6pgFNdw2KnZVr/hwFNzE8EhZfBrPWvDrX6jz9pzlMHgvQ7aUHmQAJC5N6bEfu/ayGbTue1NwSDktrUB9/a2ra/35eLu6YQAAgqqPNFRPD43QJ9Q4r46QH/J4eOUWzIo9uwK5YJ0Pdc1Cj+HYE7AcOB3oXxRO8cFn48gLL6iDlwMhtQmkCzy9y6ljrpxrUeap/ikcP3F3IhzCJNHlICACYn3jgBUZTqSzWA+BcsfzA7OKZj0qlLgZgB0rU8Knu/qNGyrWbOX9Bp2qGk2ns5sTp+SGufZtFG3OeUqoBc7LMsYwTxwsbBkNrWtRs1OtRs0NULyjU9tiYE/NkSv4TrBOmrMH9nP7Most49emhOgT5FUxv0ITwmoo+otmTZqEaR0u0j2ElzEGq0gFKndtifsuEagtRu8FTg3tJqjzhrr4roz6L/xyGoA/cXNb6JZqmZ1KCOiolzn7CnZA/NJswRBu6rN8UrYScGUDTtvswYRe4MAnX4h7u+uDhRg1sVsyo0j2EnVmgdAh3JBAB2/rd7fAPoQt+N99Ch5s3sjeEcDmuzlC9vLYQonJh2fqAcwZnXr1VqW5wv7+OLXltorYp/4gXeZffozZjt7JUAHyKVM3PuLA6CDCUddOoA6ZOcFQHdjcm8eo3pz72nOq+H3EAENgLT7lirOcFjjLDp+nkxLVp6SeV+/mFxDUhhrK0AYjOK8Hh0APUA7kgcA30gu8HN427VIGuNNkVCFTB4M+o0A9K+5+2sJhLXGgEHPbJRi7VI9OdcJLy+UQVxu43TBuCgAupR1KDdRmQ/ufax7AJlDkCR1tA2L9rW6t5gAgP/AAuy8ALok2IofT/Y3bNBs24a3Z5wBlGeJPTPesSuzMV3l5+mikP9zXobDfY6e4l073F23tXbfjg8Pqy683haZxAwMkpGp/e6jqyXB33TmeNGbPBRwkeqLoCv8SmLBOdTt0eWdpYJ1B+hJwhr0p/f3GRuDUcfrQRzgGsBRXf4Y4cUSajbdn0gaY0Tlr4HWk4pJChM/npRUQsX5VZR6xe+LBJ1WmvL/MWd9PfID0GEiz+yx3clS92uxcN36x/MyArcA+vMyrDfnoFtbW5SxJ0lizWjd4/8eDAYH3uTKlStf1pvGBvjyo0dkxub3uvMwhMpYkANY4JGADtnkBoBz1C/X5cq7UPCPf0t2I1OfJIdUMbcUixM3xLD98BA9GpVWADrPgQsH2kH0KGGq1ZVFVECtxV4vwXGx8DmHCGl8w7P3ZOFVU6w+nuixCxmzQNc0T8jMXbi6XL1NUb9/9ykCnciG6rFCCseqWUi0y5rqEqArShALVgJ0yjIjg19h/PiRI+1Z4JhYdMtsqxITAuhF6zQ3swpgXgJ0tQqjxJ0txmqsxY6gFWBjrQ2A3rCOS5xHaWp/63dXl7j/wjd+MYNeMtIaHbRHgtcBADlY86vzxHYTgfOraKuXpHYNEm43ixMrKkAFUK7zV8KhV1OLKPgOYD5sNCFNq9uRbpc1pltodYW64UbDZf8C8kwZVa6pygOkNgi+jlskAwhtnYThDsIQrKDmHIEDmFJ8YY78ow/9/lIbYMy5AqCjzZDX7c2nqONDgKW5sUBdb5IZfr9YKPDGzJgn2PQ9WGzWrN0EGML5ovVZjS7rnW5LLQkHMsVp43rzvtf9Vg0EYqFhUIYa9jS1XQD0ycJG04UtFpltbg/Y5q3baVMqz3OpjGcA9AKo8w1CauPvFn3QbxCg37V1hAwHg3KXggcQL5kG/UtrkAz4Cum5odesWtUgSAUrEq8XEBcTwrIKShdRuyuzONUW6nPFPI9AKWT3YR4XKhfcdwGImASMpF1chGqZqAP1SAL4Elk25MLamWX22Iou7mdO3VEkVxHANWq5nWrVbKNes5cNGnYE8wjjgYTnrGZ7idkFeEGgZKLe1DeAe1KzrqU2tNQ2G2a3tZXgZK94N2eDg/t6F4ZMYNC1Djwyym1nYfboHkwhzRKuuwDoGk/0QmbStqEyAYS+kXwpE6Mub1avEOuw1AVrg5Q1a1TZ1Fj2gZZtDWfQ0bkBAf8vfmY5z424Z+9qvIX933XZxJeGskJN15S0jnAbj2Tm+B+gscq8xJKbQbiOv1DO68fEcQioa3VjR2gAK654RqO2SnWvu7hj/4M4PeM+iMcAmzG94hzJMPvNXk0s45xVjQ4ZM0ecbLme747fnPcL+9Ti/SutdwDof+IAnSQv7kmEK56cZDJmUbYxAz6jnDxo6VDM+OLFyCEY79hf/T5iBOHPj9xbjAWTAd4XPRj2UBzxMerifZyY6AlW3i0toh4d58/Eqteo0wne3dx98SlN4n59dZO4b3rJN1jPDTmhYMT9MGW5YsbOPHgMAKoe2uiEggRbwrlALwb3MwBpgL/jPoA0Hp07UNaC81bSU/ed9j3thXJE0KyOGAnPWW8i+dawrVbbenXdW2DcL0z2bHc+Ydmh1GZ+T2CeI1HJbiGZDQDQO2vWarSt2+7xvRJKz1WOycd0bp9/+qMrzbnXnzxaAnQvfSlJpDLFhk8WqlOoCbDOYyxw74qsQjI3sb253xMuX+ffssz25wsZ1EIJGmo7X8eh1oIpM8ZFppk12+522DpVNfo+tNHa16X4RbLbR6/qYxT7WuzbOoxfowDorsZ6dGd/qbErjnnrH8/LCNwC6M/LsN6cg8Ic7sv9evvb3/5lPTWCh5ce3mYrMgYNxaamGsuoSUebGzASoW7tNiX9Zb/oOiS7cKbG82s2ASHuuWfKkulgmjO5oPcoZYg60ZCJ6nWFzywjdrTbEUAHi4dguWCTnPlCf1a4x0cysQ4mpalaHtWvC6Cjtgq1ToAIg4bSEGLQczt3dbl6mxi777r7NgbizPCGCVyVCC5qqgOUlLKkqHmMiyX2y/ssOwBXGkKpiMj0c5QKV3gfM1+VQ3rIJddd2MW2OWtcGFm5DMp/Vh26ADplnwToJatEBp0AvV4ANrQ1+877bxCgk64oIV/J9imw3Eky1pk/PZ3blfnCLuBxltjVxYLt1KYwvwErjWDWN8aovULADWn+EPMUwTjrM9VWDL8/Akf2ZtNODwZky9dg1OLmZ24tVWm9VSZWKPsMt3w3MIyaYjB6UlN4RFYDk5ixZzUeY6PG5fkfPvJHS22AJUB/kSTulBXMadC2vy93c/QK56brdBFAOoA7ToeKGJgbJbl1wFK2BbrpVltvWF4XON/Y6NEUrt9zV6QSuT7nuuK3aZEc2L0KBn1hk4mSA5uHhjYY9qzB+xd+EZrjsUpUsXgZXDiQjV/cJIB+evMQ+9AGOK8CozBoi3OL+5n3BueXgDqli2y9ljDxwmpc1AdyvVH5TTDgLU/2oC6xOoxl3q6CtKNrgQfFUdNeZcCZxEM7S2cv/Tb3MgcFqFGehOcFwx7lLvj7qm3Wzpy+s6I6UGB+AhL3htlXDBt2rFWzJvaIPLdnpzW7Ojf7wjSxp6dou1W3CSBbBi+BGoH50WbNNpu5nWhpDKKtVbCbMGhD0iupAUyYPTI2gv5np6jvRGtDdVCo1XH/C6BjDcAaFcG8SgjcuMrlwwIKJUAHbCZAZ9JO5TxrTa2ZrUbOJB8AOpzmf/qB5Up64p69vfGNZjV4nuM+FCT37sOaK8XskFM15gpYa3HQSPIAamsNx1qG5AKNLgs1GpLnfCa/hzUx6ng9xmCUgx8vZosYdCRFCKmhOhKoCWeFEMTrFVgNxejFvh7nH3Zx3MXjXnUePnPBM/fdfGEPJDcG0K/tei7ePwaT9X5Tkc2sZA5dPCeA7KZstBsIsO7+LM+VeIj1CAC9svwoERRvg7Fz5/iobUe7tsJN/qDMyFUuas8W5xlJApxjKALYbg3LMdzhu2ZnTpr97AdWB+h//WVvIUBXWZ9mGXtnG5JdcFRXK1zMrN1kTmC+j3WN4F1rScQfYtAxT1KautURS6VT1UvjeRFvhVEb1kyYQ5KBL3uxYwDo0VGr23a7Rx+aMIW7PNunFJ+XONpeQtUE4Nto8H1w/sNm1w51hjz3BCpNAnKx9liXyd4nc9u5tFpbxNccP1x6fXjGJmKqmEbaQ7QfIMEqY08vh6tLcYDfh4kbPjNqz/F7KLDwOpjGQW211VG8GvMtVJQ7M7Sfy4ok8bEBDJLduCDqyCtxYySVdTt4pyDfaEthe+VG8aC53I/0Ouwdj1xbLib+skDHrSetPAK3APrKQ/fn84URPLxwe8tdNj0rTYpHMFkZU2QlVX8ZJhVw2QXLQeMsynXgmgkwArAeNXXOBiNzi9phT0ljIcMuFDD9+oUjJw1YbmTNOtjwBtk9LPYKTrXqiDHETpoaX4cABrJQtvHA+QkAso4pS226mDGz30fPNyxENInL7fzV5bKFMXbf/aIvXYNeynYdNlfAOj9fROxR0+S9L/E61R1pGMoNIQKskM+XsqYSsDtP4mNRmsHpOOHuHr8v62a9rzfdkCGNrDFgBfMM0ydca9SLA6TACRzXDwD9XR9avc3aL3zjG3hMkYqaM3BlR2AAthwbLozf8D4XZnBoxzdcaFNK3fGtZj8KVsMcBRn5qDXH+UMJAJZs2BBLttVCjbn6mqPWfN2BORh3gqEi0PRJ6HMt6oNDdVAYsHiwV7ooyNU/JILYYPe9nylql5kYSlL7Zx/+w5UA+re+6R57er9hY3xw9rDNbG9vQjAMkA6wCKCe0+wNwZQAcUg7cV79bsOGvaa1Wk3roAcrjPNaSGzVreMlDAA6B6jeCIyvW+6qsSjeYz6eMxkH9hzn0uu1rYX3cIYlgG81KGBYAJmfj7U/8FooMKqaxKX22RVN4k5tHCJzUwTa9Fko6/9wTcMsLs6vvIeUxKoaxYFBD5M3Bvv+GSVhFwvK3vHNlrfrkiw97tdov+ZaIAag+Fsw9sFgKmwK9VAEX9etER5IRr06AKWGTkeXcdLqDPodp+/w5KruC6ypW1Qjmb2wW7Ptptlmq2HdRs0eH2V2fprbE5PUnp3BmEotz5g3ymp2rFOzO7p1O9Qyu70rFmp/rmAW5817CUEuknSp2Tgze2JWI1DfS5tky2JhdOEUk8w0iWoqmcIyDr+dS6CuZDFLGRzoAuz2wEpjrfM69I229rYOZMhkuGrsdPCzDy2XkKwC9JwAXW3ImKh2ybjcliuJWAfFpaxc8xOvgollt9awobWsg8ca6m4FkASWUcZV5+/xBXd2/G2HAB1zCEcQMGcgHi0/fX7FGhYC/7gHaLDn/+GdIrkQQKAs2IpdS9y6ZwsJ0B9M3rfSevfPvsfswc+Z7e55MTL2i5A7e3q/4GmDqa7UkzupW/S19uWk2FejVjf8AvkJHIDHBsxkmZMXXDtczs570s0wq+7wVflzsc17v3atEweTBZFII3fBBKsZ8qMnjpj91H9YHaD/za94KwGg1gStLWN2uclsFzJygnVERHUbI/GIXtqQYlOi7r4W3l6tUAr5PASf3YK5mzPtAuheiOFJJwBzKvg8BRWkRIfJdD0fc5v7FJVmM8rbqY3xzhPs3+3ZDPodZBk/U7feYikZlF9MNrFTRtTJI1mysOnVh1eac284VUrcfREt2Xzf/3RdawWDLvWekynekyjUnrifIl5gMqHSvhiJiqGXQlXbKOJ5SOirf7nmzBYY9AZMmctyFd6DlQ5BKo2s+LoUSaVyvyhiS7/B4xjxe6x1D165utTY/flEQX9+zvoWQP8zfq3SNLX3ve999tBDD/FM7733Xvvmb/7mQvK+7OlH8HDH1gYDG+buadOJIwnwsp6cxlwIAhA4Rs2U+kiz5VQdwTWCLwSvdZslboDBRUJtzFgbBDMpvodX1jmTXoDLIrWsT6I2TngfmZLVPQDTth9hDn5QWxAuWWgb1Wxa11uBsSaRAFAOzPuzKWuuuvXUFfEC/BeujZdajGLs3v3iUzITqdSgy6zpoPGVK1sLA5O4VpGHULYzviMcL69oBPDxm+s1CMF4qF6ag8enBpMXyofrAXrId+nmzhptGAIJpCPEw2OP7LqRdSWLyDS/2X6S2rt//7NLjRvOqahB/8avtl5LdZls1APHZgDzLCdTjuM/O53Rjf3yIuHfRnDqJzBX7V9IlTEPwfyjNnMYMlXMT/oN5GTFNptyoj/d75ExP9rtFE6n+ECxmcY1Keu8NJaSF5cGX+EOH4ZfPmsrPJMYPtw/aDnIjdmTVTCL+Se//wdLjV2MWwB01PPmKSTFme3tT9n/fEppe8pvOLrz33Rm1/nDlRqy9kGvaeuDtnW7bRsM+9YId3aXEuqzHDShKdeGcjYWebRqTi1BKYmH8zAGCmO5SHQUPFw1F1LeMxEg6xQ8griOQV8VoN+2sc3gLq4nHiFDxz0M4I4gCsmTcERnYOTSYoDzqEUnm4TWdG6IiUC3FHNKisxQzVUsYNApnUYdP6Sf3k4I6yr9Da7rxR5zK34fyaFIXJT3u7eMq4DwOEc5zZfqGwTCN1KDfhcAOm60QqSdW59y69xOtXPbaJidgGt7q2EP7yX21CS1s+PMzs3A1mJPUGCJm+h0r24vHjbtaNvsBf2azSCFnwo4oD0RnJLRQm2a1+zcHN0aavbsAr9TeyUCQpcUBUA3lDMhgdjUWoZ5jtrfWIs5nT2Qx/Uj4M3FkPdZ6W3WyXMmGDbbWgPxb7bYaiDgz+y9D68mcQeDnjlAB4TGVQnQgvVWrRu1L0pSHAw6YLzL7dUzxYa1tm3WejaotW2rhi4mMHGFTVtq43zOpNCw1uN6cw0mcJbalXzmPc/VKgrPZaI76tKL/hJeohOy2WDixMe5hF1qkVgFdIeWiLNUigjmYx8HQP9M8n+ttN794LvNHv6CXNyjT3isO/AcKcwTK5JxsualiKlUkgSDXpG0B0AvJPEB0FWN5HtMKVcv7/NKkj3WNFdphBEdt2LfjlmvnoT/R7G8Knao1rsjQZ+4i/u22U/86uoA/bte/ld5BpJ8S9K+n8LvIrXdNOF+tEB5E5I5LCdTSRlMgQPUA9hTIeTsMO4tJLYxk1Sw5CZ5KLEI8zNFetbJNX+DhUcSHMfh67HWMq4E260WcEjOgaEP93acJxOmhUO8WHK+J5VENQJ0MceRTvLdC0Z11z6/0px70x2neH7xVSZ0S1Ik5v/16o2DZxFRQXEodUlCu06UDKQpE7dwcQ+vkgD5uGYjJtxLXyaUGcqTxJ2ain1X/4gSqCizjLgz3r04t0Ihr9RaJF/i+bjen7x4aamxKz/hrX89HyNwC6A/H6N6k4754IMP2lvf+lY7d+5c0Qf9kUcesSNHjtiv//qv28te9rKl3ykC/jNb6wwXuJlgZWRWGq6bMrWiPN2zqeyRXbT0CuMsVgIyr5pmYBqinl0GWwDoXJS8Bl2SKmygJczEIgE5KBYrLEDBGHAhjiXeiw1DJaZ2aQg64cQu85JFnrJOGiZXYtBDsiOAvjedM2Do1CSLRO09lrorSxpixNj93ZeclpFYpfa8YAiroKUC4K9fDAtZki+w1WympMm+4Whb4v8iqIgwKbYNyQzLLSJM/WQSpzFXHZy36gmXVgfoas0j6WS4uKP3NwNeSkfR+lTXDTXo7/7wjQF01Dpjbl2ZK3sPpnycZpSzo5c5epuPktT2mdkHu+atdnzzZP2bO8+DAUOwizZpYv4RCNTJmKOu/Ei3xVrzQ6iFZts4iEEFojjnwyzMkxyR1Ihtulr/ixEOEMXgOoyTgsV0uAaAHtl9zFuZ85jtLRb29+5frUdrCdBVU4/rDXAOKfsc9eYAj+yHntp4srDZXGAd9zHqhpFQG/batjbs2GDQtY2NgdqvOWNelVSXi0ok7krhR/yrmJ+hEPHoQGfmkt2KMiGSWT6bY0qXLQcrWf3nAuhQCjy8IoN+cn27qC3E++P+6cP80NccXOuoKQy2p1paonnia4q3R6uaxRVBYkwqOhertR8APmsUo286pMUO0EM8y/emAaYSmbrP5eJeTYY852JfxpOSuvPYHiBG67EbYdBP3c5kSxmiinmG4PJwK7d+3Wy9rdKns6OUfcsvzcyuLWREhG8mXS2zO/oN+4qNph3v1O3Fg7otksx2pkp6IDEHpvzZWW77mdnZaY1A/VqmjtsyYHTk5OMjBCDaVKVQZijDkspLZRwxFgg+AbapzgBAB2AIsJ7n1mvU7HBHPhTtuhvXoc45S+29Dy+nGCqS4I03W8oa9Gh5hjVWbCPryckyBjj33ZQ16HgGekkn1q81ba3WsvVa147U12RyR8iOfU82cPjGq9EWDUVfO1liM0vtUjYzcJ2oPsM6xGcz4SiIfv29rGOVQjaANP0XYKHcYzQvK5Rw7FNeg071Wr6wz68ocf8f/67ZY0+ZwQiOdjOV5DfbrjlIp/DPQXklfyNwXgXmAc7DHK76unJaFW7v8dFCUFRl6zUPffT8MbbgKlArGHfUx3tteiSOAqDHsYA/4ZvbaZltbZj9b7+8OkB/491fL3DrTup4VA06mHSRLiz8Qs20G7FG7XnEFuxG4z3JMY5I4iOZqXgiPB18VajspYzp/LURl6icQ8QB/o72a5inkoJ7/3V/Dd5TbvMZSxkEy31mUuQJQkZ17nz3cho76MxsvvPYUiAz7tdvuPN0AdALBUQxr+OtNEJFAjAW5Urr3Odap1lK4gkQPMaYXr/P4LgRL4h0UVcdjHk1Fqm+R5AIsV9UE2fXn0uo1ZR8KJPxet/MPn5+uXacz7kn3frlTRuBWwD9pg3lzT/Qa1/7WoJx1KLDMA5fV69etXe84x128eJF9khf9qvob7u1Tk6Ea4Azr2qTIWddZvC4QcscLpyti+w7g1A5qINJR0/yaPWAhQLtM6oS96p/uUJkBbFrnbb6bbfwWLYIQ1stupu7K0ywR6oNhcRIhiKon5qgNgpJBARpNbQC0bkjIEPwtztZeFAm1gqsPI63sz9daSF/z72nvWWGDN4UwOsfZeBSyVBysyzBYCG9rMjmmAm9Tk2g55WZ1JBESkpbye9WNwc3xwswLpWYB4Hu7B5gIVzcAcxZ3+gOxxhjgFomTtx8yY2BKYl790eWk5BhXGLe/fw3vN7qzRYZ88dHU9tdJHZ2MqeEHeZvYJ33wVICmHubNSgpyDIViQVs9giyUUOq9kLBmMPwCc7zpwc99jE/2e/JBA4S2IiGKumMYNCjtaDGJmZo1SlbrCXGJOagchYHI0HNgrLcI+YELu3+PLHv+u3VXNwB0J/ar9sUbdYaztbG3MO9AsZ+pvpvzOvRBH3Kc69PZzsFuqmvDXu2ttazw4eGDHLLsooyaaE8QzXULIPRSrxSzvfKU4t/IpgKdHSweqUwiyuk7deB8+IYFQb9RgD6ifUtGolFMESDHrLbAp5VFgZMZAVnuzTaTZUicQbXe7q5J96BwdmdglGTsWaw8wLoMI/TGok1KYJXfNYwi6t2WSjvbr8SRc9ugTaT7ikAACAASURBVHe3DOI7RhcIfBaarhXlQAIpNyJxv/22M6ztjrmCo0d7skHDHMxKJH1pltnuArWWMBAVOIcaiuF0ntoLh017xVbLTnXr9hfWGpYkuY0mUJjktptkdm2R20P7C4L7RycC6HPUsdKs0IWg3kkhLhKBu4Nx/LPvvdjB1kEWGl9MOEPNhfUUclkz62IPwz2dplwfjnVRw612nFRBuALrvY+s1gf9zsZfYWtBOiQ74gPEYSs0Zybj5zDliqRqbjOzfGrb9Z4dqQ9suzawE/VNwuWE0vXU5vlc8v5CLF8nGN/B3LTULmRg0jObuqkbEtnav2On0Z1W7jMlOFdKBWkAJXyqYvdIGoU5Zpm8KXc/7CkwiXsy/Y2V9tj/+T1mT503G02UXSiAre+lVEWou5/WoSpQqyStCtAewLzi3l5d4qLWPOaLm1zT0K0KoioNA/hUAvggLyrBWHRncVFNYVB3PUCPvwdAh+v7+sDsf/n51QH6XadeTTYcc15ydswZJRkjWQPlCdvsupN43E/h3o49VQ5FuspINLbquhcjRUxzyyjR83WUf4UbPJOFasCmuMVnDc9BJoNBWqg9oFSRBJkkeJQcBhhXKkg+F/guDFmLZLDWRM2D1Ca7T6w0577xrtO+J1QT0j6/fG3XGl0m/ookQTEJDyoheYcV+5vOUR/J78JKfBdTOCKVOIu4P8Wgl73Mq0or3tGRr2B86NHOwUyDn4pnuCrzFf/EPvRHz55bauyuO8StH2/yCNwC6Dd5QG/m4Xq9HtuqvfSlLz1w2AceeMBe/epX22SC3Wu5rwBKpzbX3Vk9EumeuaQUR9Lc2Hij7ly1a75hO0CH6EkLiRh0kWZYRBQMkbXg7wNkiSlH0ArZ6Xa3Sxa9ZNC1mkUP9rJNjj5nBDDYHPA9Sua2u0BPdZjHCJx3mg6WkUEmS4MA0V1wEew1mwyQr+6tJnH/hy89Q+AarGABNPxSVGvzIpiJIFpJh9J8jCyTByBl2KSRV3AkJQNGGMoAPR7k0IMFjqx+mJnJXTUAegnUQ+KuHugyU6OvAHvtSuIuZ1KYMAmwIAGCr0ma2nd/ZHUG/Se//nW2m6DePLXPE6CjVRqSLMju47PCvVmfOfTDrOl1Ey6dLySqubVrqM1UjfmA7HjDjnRaZMmP9rrWraPlFXr5yt3dt0dt5P5TCdCl+AiQFHdVlCBEYAVQXpxLpagwgLnicA8WiltTyA7mYu/4v1cE6F9/jz01qpNlVN18GURE5hytyHBPTKZg0BPWo6MuPYGbfLJgqzz0JAdQ39yAUPm67gN+AytoPAjQv9QqUw1ANK4KlquOswfsJSpg/YsB+kF5PY6CJNx0DDO81Rl0mMQV6RkfN8jOMa+kfCmuXtGbNsKsuP78bM6i89704PdAYo3PKXuXi3HC3PM1DzX/XCBLNr7KrHDYC2PIMHTUyOP3EQyXvKW3bCvYmUi4lNcOa8CNSNxPnTzDILkA6FhH3TgPNdqofNFcgTQTZR05DUNhngVlFQCAql0zStzvXWsUDDrnBcowyBXX2e/8wb0514cvTOs2zWq2n8MMCuUBzmDFfRw3gLc2ZBkPGXQlFcGkQ0kTST1ex1AuZBkBMmrPUfpUT1PrsU9zk2selDiqh5Uj888+vCpA/y8sc4AezLQ6mEPt4+3LKvJ27m9FD+iJpTaxrVrPDtX7BOjH61uW2MIm+ZQtzsZobQZmk+kGzQqM5SJvso5/l27tAETyx4AgHvv3Al1MnCsv53RUjweLXpoSCtIz7eMi+SqQL9uvaaZqZcVegjZrT6e/uVTAH/HJv/p+s6fOmY0narNWXVuidVqYsxUCsoLm9tWqrIgoWHbuZa5Gqy7P1Vp0vpfndsJ/pwDhDsb9pizqyq9n2IM9173r97Azz/G3SrdGxgDJQr3SN9fM/tdfXB2gbx97uTvzV/0GyjIGrg5MWAb8LtcdGgBjv6Vfg8Ay1IrY/2G+S3CMxCaTu4pYgpzgtSf2E0BnS13uqbHbCuzDA8hF1jyOCmFkcso2cJXBoyEkMzRq1InzUvKozMIEUNV+ltl4/6mV5twbb5f5b1yzqC+v7n0q86ikr32pvX7qffF+WTnf4o+hkDq415aFJFIJyBMKZJTafLZRy4/yLPjueHlBgPNit6hI9atz8PpYtXqeOPb9Z5cbuy8VF9z6/c0ZgVsA/eaM4/NylPvuu89+9Ed/1N74xjceOP79999v73nPe+zTn/700u8bG+DJzXUuuFqNtFAEMA8XSrScoYjda/LUDgjgXRJeWt54oIFHhWnKzrHK27O43LDZkkMunnjfTTfrOjYYEAwG2Rbp8pA/gUkPli+WRaw9AdCvzWfsOQl2ADKudiO3AfqNet083Kt3gGpyOJSjbVudzqA414s7eyst5D/wsttZp8iNvKjriRDCF+JIjfsVgoQsQLqSH/56OBNX6ogKQM8aQ403HvFsyNBDFhlZWIKFaEFXmFSVrU/w9rGpkB2OmjJno9nv2bPlpXRcPdDFoHsLKUcFAOjv+vByASvOIebdj33ta+2x/Qnl7I/sq5c52ikBmKc0A0SwLmkbgChl9q4KiHpzcFJkzGlWlVPKvt1u2la7bbcPB9ZrNGyTBixxTYJiEXgOaaaCCyWc4K7KQMMvX8xHBCE4l9heS/bcVSUFKFWgKrllyHFj49WmPl2k9p0fWA2g/w0H6OicUDDoReAXQYMHL87Q5m4aN57MbDyZs90ZzOC63ZatDWA0VTJP1RDhur39/3eNKV5bVXLEL31Iq0A+3rcqsePK4a8pzJacQZ9Obgygo82arnuELwCZAMt+71SuL53bry8x8WC09JmQmoLr0nV5DHwmShgdmIdkHscMRh2JSfw+5lwV+BfBIZNKWCtd8ky5NRRBZQ92GjEVrRqjftQ/Y8EYCqA/cvn8Smvd8eOnnbHylmieIGSNdlvlSYW5mLsUTeeZzRaQsKoNElZbsNHH2jW7o1ezw+2a3dlTy84hHO/RJaLVIfv+wM7E9pLcnpnXWYN+eV5njewYahDsK2Hq6JOUiV+/ZvgV+1A7k96D3J3MX9zUPuGzjN0q1rBeOoOOpMMG+g1z3J3Ng1Q8Te2nP7vceldK3P9LMwJ0JYEwVdRZ3FvzeU266nUjSaQ9d56PbG5j26h1bLvet63a0I7VN22az20v37dJPredfKTaXprD6TPC071tfctrDVvkAlS592IXME9tgtp0OrgLYMXZyVAu2M0SqMeeBFAvL/BQjnlryYoEPvImAdDPpR9Yad79SAWgo8Spepux7Rok4RAG+XeVQY/FmlOk0uoscqdKIFRY0hI7lWudh0aR9K62TvNcSLmYVFZIrf//H3tvAm7rVZYJfns+8zl3yA1JbmYjCSGISNloM0QJIIoyB0TAOIHEfqottMCnbAq0pIuh6ymr7W7tUigSQGUQaVEQZBaZlFLQBEIICbmX3Ax3POOed/t+7/ettf59zr337P/ekyeUa4fDvnvvf3z/tb71vd+YMDiyyfDiWs/rh4EhyDzcT5cEfc+iyH8+gxB32XUle85RubN3jjI6StzjHGmlp8BpNB3uQTszwAnSZ29yjVxBWDziYazekBk30zPgZCD0MTIJpN2rwpuzxrpeOCjei10NaDAGuM3PD6z558hRRzeWSNBdtvIZcWOkVq5ulJN1j99/XqjiroZUX88DXtSl0pcNsdCFIVxTtNOHZ1B0EVjRQNeZk2cVzgC9HDnpyNXX6u9tjdqabza1K8lso6X1TeLdn3apDhukBQ39S+j/H/jmXRPN1+2fMW9ZBoFM0Mug9iDt88EPflBe9apXyete9zp53OMep2f9/Oc/L7/5m78pb3jDG+Txj398uJKFBYSsn/7lysN5iwvqueZaYgTdemZqyKXmRFKI6Hs1hvtqTrB2cWJV2hDabsopM9N5XAhdeswbqmyib6QqKKo41eT82TltQ6HCywq70WvMMFDPDY2BQ1yqlaCjIE4XLbgouNYGPUGx8ZkGlTSEOHYHJOhqqR2Z5RFanIgcKtlm7TXXXCLI0cYrVfoLgttWahPzoQo9IwKiB51t1izLygqSuacM+Y+awxwI+lievnviLIdVl17LjdY8KTMSeF61h7o7yaQnmL29NczMWrPheyfmgaDb/aIf9Ms+e+vEQtzH3e/80A/KgfWOVme/fXVDPegoBKdFajQYluGgUASgNHtldpBzhNYCd/zNVkW9Xgt1VFuuaT9zeND3tqY0GkPDiRP1JIRhBu+2Lehm3PDWgqGWgD1M/+xGDhbVIzFSa7vVGVDlwLyfvoiHc5r6gGruN+wIQedc9POpAmheCLz3un39Q0eEOgrDoe95i3OAYzgaF/y77ZL0SM55pOLnoqIaOLv9IxL0SM6D0QCHc4IOD3q/vAf98l37GC6ZeKg9TcSv2A0I6mFz5SxIx0iOfbvCvNdaD9a6zmQTLz+m8rAYEMk/5C621/mdGDVClwrP2YTxzEK7cSwoUPCm+MvP6bnnOgZTS4jNaRD0bxydLLfQ5+u5D9tvhd4op/ACgcV9TKHmB/K83aAxZN5quzfSyCW030SHD8qXkexuVOX8VkUWaiLnNkdK0BFaDoI+02hqHYoD613td360xyJx93Uq0hmi9oVFJiQedEb2EEutkwFyYQQd5BzedJBzkHR9HsDDPHrwni/CUALZ2h+wWJx1KIHnXf3F6BE96Jcm6BfXfkyJcozRAA7MOtfQ9iQHPSr6NB8OEZw+6mhROBSIm660ZL4yJT0ZSFuLwPWVpONYVlXD5B2M4Kgkgmr4RtAt3FXbrKGNquau0/jr10YeafnmFnbr3NK3Ycg7yFccC5B5JPAk+97LQhEc9eT+4UcmWit83L3plSL3PSCy3ua5eGyTV+ZkdU+6O1yDzLIN/bOT5kBHox02RCKNlThgv/VkQXcPuhP11GCQXpsT+uAEPgVB9yJxOm8tB72JrgjzIm94Z3kPemX3I7TOTkEfsdHhRQmj7Lf6Ni6rPCRdw8mZI450RY/RZ7FBao2QO+ka63hhXEHhCjLSCry5KU/TVcywyAXIveZWI8LmKfcngJi3GmrvEVj+jJOyhToOUTy1/UCpMfeUS/Zv2WYtroWU51vZc4LXO4lsi3Ya1wLjniTy8Q5dPsXnQucBZtWKtmslQYc8mm02pVWry1JrSmbrcETQcTMm+seef/yYjt10I6wv7/vGnRNhVzhJ/nDWEcgE/axDevYOmPYOjzk2nF7pZw1jTOOlTnEJQfFaWJCWEWPaymMupLb9QbVOI+ZO8Pyw3QG87a7uMgQaf6oojlivFiRJhfhwqB7z+WZLFdPF5pQKkpVOV73vF83Pa0VlFu6it0c975aPFENMed8MsWcoNAj6iW5P4EWHlfFEty2Nmsh0g+R8vlnT61xuI0RIqCxWq5r3jou4++jxiYSRY/db11wa8htd9LqTJliTLVfS80WDBz2pDB6IuBFzb3+jvUiRRwhLtJF3HKdZYZEWXbZCcTkaMfAfiQWFdSronVx6ET4PGWehMyPoiXcdj45hubEdFYgprgFE9hWfnaxoEq7FsfvdJz9eHuj0NOf8DiXofTncQS9WFLLRgr2q1OLkIOiw5qOlEzxeKAQ3UwMpr8muOsZSXc5pNZWM49/q/dIWe/FFKIp4cAJxG44veDNZORUFbBiyFz2u2M4JuYYAamVv1mBwJ6rNSpsLbrxJYjMrFdnoDeRnS4a4uwcdVdzxLEK14sLN8sbCPZs2oU0akugAupW4oyoRCUYpXD6MNpHucQ0l2cAJtm7i37tSZZ+DWhK+TxSV1CkNmYJxsd7RVnK3fX2y8Dsfc1fsOdeiBZhLqfds52FhtmKROH/eQelBakcf/c+Rwcnbwn4kfnwWOC561eK4JN4MAcWLbYgiYdfQd6v0z1gaPg8tjKRdMzj3cEyfuzjOuAddSX+VYfr4jXKBirHn1asifAZF4s45d7/mqsYIJ9w3jbeomA6C7ixHi0phLvWQWoRwd+BGGQPPHDot7K7XpFkZynQFhT3hhWdI+nyzoZghDQRe9/aQ9RYOtNHBQWQVBQ+t+B7FHDGDkdeLYuJrHEuNsxbqrgQddTSsMj/WNNBXpMTswQVgvUFfe5O5mN+oSI/t0fcZcuEtJT3o+2s/6sG7Nt3YAtSSvXTshKgm9aZTpcf/azFUjVZACDviDxhe7uPPaEtYC9RYEWKDOPE9uDgQJRu743nlLkKcwHsxKhLvOI2doIc0rVD8Lq0Mb1XclcD15Mjwo6XW2P/0b0WOHBVpd2Lfc/eIpxfl41IN8ElIeyrjXT9JSbN28bJhhGlcQ3p14vX0SAA/vsvCcYI+3lot9YjrlEwIelgrDNQCyYc+02UEyNyMyGvfWp6gN865RqrIF09ScqpDerFRZBDjz9uvefROWO2MLHMum2nGDHBa/FRbnPpqZ0avsAZ5ipIL+nShYOY4Lop1PkD0Y9V4hLZjvJOIcyzTgMTOC9GczJNxhY6tZ0NNj9FQljuTtQrzdeI5V1wqjVC3wjW7uMAW6XS68Nq/T8Z8w6ZbLJzJOpx6e1ismb3dQczb/b7qt9C/Zuog6DU5Z3pWFltTug4BU5f/W1xZ8QqSy3CvPjaAEeCPb5uswN6pzpV/O3MEMkE/cwx37Aif+tSntn3sJz3pSdvaNhL0eQ2PIZljmK/muig5ZogSq7dTAU1JH7zSUMDch05PhrWzQRgSlCAtLMQcdAiQBXg20X+6OaVi+thGWwXshfPzGq7jOegoDsIQOqs46+0mbOWj5d8JupgHHQS9K8vdDnt315GDWJHFlhH0DnJzkRdZUW/NHjQbHY3k9gfKCfL//VGXaeExvJzQuMzzd1WszbOGf5B8W5i7EUPt562eYwuZtXcl6NqOw0v6mHfIreJGLjx/2lMAXJlPK3LjKY2HuHteppIAL1ZkVlhaY0kq9N3IgpKMigg86L/8+fIe9N+/7gmyoflUQ/n2Op7bQA5ttDUnHf3OtWq7hbojjQAkfa5WkdkaPG91ma/XSNAbdZmtV2WxUVdvP3JOGb7vJox0MeS/df203OoQEmfRDN4H29M7QgqCjTsnS07QHUMthKXHpVKs/9N/M38vtWrDg16eoF8hB9ZqoiHuCUFPJ30k10WSble2WT6keezmQdmsS2ytloT8PCOEjnYgcgVybsdICXpCxGP1WX9IZiAxgr6x3tE8+rIE/fLd+4KnWtvoWIV03KtXTvc8Sr9+KvvmMdHCbgNVjtLfY6QEjwM55iHuaQg8jXcxwsFz0x1rJ5h+7NiFgcq0CxqEOeIafCzTK892SoGg24/uv9ISbcOh3HGsnAd9774LNC1IDRE2vt2DDllLwmL+IzUEibQREdNHiyfmosOQpYUnYRxF2D7WFije8HrXYIhgCyZcc0fTM7AWscL0kR7qkbAVFORnDD3lc9RUHDeGalcQpp7Agw6yrm1BzbvOAnAICSdB3436GvD699HWaSirvb4eH159RV3Dewdyc8kicRfUnm6dUmJVkiJBh/x1uRWfNUYKC3cyJawpyCkfaA65R7s4AffB4ZScCTzu1TalvRAgbsVKQ7u1GPfiIe5p94AiQfemaxYG7+lVXmhOL8YJOp5jTw6XJOj/979jgbge0pnNiBjkWyJbglx3wpbINJ836X46x5KbcvKOYnDp9mH1SK0bGH+x7mCYh04W/Qs9vIXeh1IeiexLjQo+vdXgDmMW6r40RF75f54BQd99dfCgM0qJkS14seGj1wkatxT4Z26fenj1F+9ckxB0pjP6y4m7C/fi+GN8XNRHQscA1fVIxN1TzhomGoMZ2q/58/HHF7oDW+Ql8R/KiZIE/amJBz3MqzFOTRg3M/FUD0xJ7/j4dKTGI9RoHKLe4E6ptjoNBqrXQjdf67EtHYr1QR/bZwQd6VowLNAAFY0njpdb4xMtKPwUrgMydjCUP/nGZAX2wqPP/9gRBDJB3xFYH7oHTQk6QoOp3NCSCYHA1mpcquGF1pBeJQSmhCF3Et6RvlU21lA9KlkQDSgKBGUM+4JsIyQHwmSpNU2C3ppST+WhtTUVKPtnZ9UDOl1DgR6rjmneJyegVBgYBhsIuoZ+V+RopyMPbLRlvd9LCHpVphtV2T1VU+/yShcKmGgRI1ge989Pq7X2S98uFwr1RhB0XdELS7MJPbMAB6WRYlFzrD1/Stuc8DOCDTsWWuiV3rWHtSndrtrRE59kK3phqCR31/udj3uN1cBinj6+03ODK/PwKNYSYDE4TV0wo4x6zo3I4B8gmf/b35UvEvfOpz5Rphos0ofwdrRZu3NlTU78c4Xzu9fb2v+8DS/kSNRbjrBT5JcjP/ScZlP2tpqyUK/LEtpkqSHIUikCt/PYxFRtSFQNfRwebcB5ymgE5porQVejkOlxvuAZXhjL9KC7d5OqsefohZVPDTIWAWHrOTzoP/eRj5XyKF3/5CvkwCoJOqJEUoNZutinJD1VBlKjjd11KEgWF3LzqBed31sKs3EFAxulyqjrMT5vC2pNSs6T8Rud/Ebox6q4f72kB/2SXXstVYZyimoQXx5VQoMaW5upn9JuAIYZ91LHYkjpuGHUEI6J7fzlxR3dQ6WE3YyP3t6Nng8vyMh8aY/USD3nfkw3EnjLN5/HTtBVcbX89zT6Aa34bj9aLi9zzz4WTsLx/LrZegoyhO3IQHxJClm5faMPuQ8jLggWtuF8YYQL8r45ViATEb5OuLkGxQgA+pKHKvLYRk3HvN2Yj+cWQuT9OxB/JewImafRTgkj2gxqtAHTZKYrSJGpyG4YIWFQHo5Urh3aQOV0a2uknj5Wfn/3HZPJu5BGVn2a1Coo4BZHPzLQgyHVU8TMe57O6ZbUpFWpSQM55ZW6dEZ92VBTrud9M8GM0oc1/ccVcPe3W0yFGQ3j1XhYepojH6lHbPMXxrROcq5bWhlcC85BV0ijjfwTohIQ4l7Og37Tb4kgvVbFr1kYN9OizaLJ+VR4T+pJpBE9gXdF+xcPNv45raFhp0tlX3pNqVzR4Bnbt3Cu1EgZjMb8B/bBfEKxuBe/tjxBry99t44oHXUWxeNWCYPS5prpBcHAEskdCXoMAKCRxNpABuESCb1LVNUponRNRn70oMdnQ12DhmwWmNN9VZ56S0TPX0/HLdLgzEQVHoBdu3qcyzlevu/cvVGGF8ZBZOlpx49U+6MzxtIErYiJrWLJIOVxPDrDoy79O2/bqs6CIXLPuxpJCd2WXYs4Y7VbpIicOz2nHnTozwh51+r3Sbygnc2cSLEFCY0BVg/ApZN1JvngXYcm0k82z8D8zdlEIBP0s4nmWT7Wpz/96VMe8YlPfOLEZ4xV3OdlvolKxnFZUYudV99EES4j6LGyNZVbkF2QSKNyRk5YtAUt2qAITaHXuFaER9AyK4BC0UNhCyVn3Y56by+cm9PQUO0ZbNWCI2FKlLbgNWZeDgiceljabbl3va3GhdVeV73+UMygoO2Zrut1rvVI0FfRZ7RWlYvnp3Uh/OKBcl6l15sHfSuFwSm0K2FcwrwCczTeA2f1lKNK5zhB14rDaD9Eb5JibYo8sISinpIF9e5av3MX/lHZ8L7KXIydlGuRHAtrJ7mMvxVzZ207W6OgyP7Hf7h9YiHu4+6dT32CTCtBF/WWgxBDMYb36uB6R1b6qCXAVn/IKdee5s26LDToNV9qoK95TWahiFubm5BHqS1jfGV1dZXvXiHfdbB0cdTfzXjClleRnPszdkycoLPuAp+2IqunceOALc2mpTEMEiHuffnZD5dTWK//YXrQQX689V1UG2IkR9AxI1N3TpPqn0EZdaKTHgu35F6K7QqYsL8DZuGVDLMMfDdR2KK1P+xi//BwfJ03iLZYYw56WYJ+cSDoNMy4AYrvYxXxx25YiaUZDjGGqOBGbzhkFUPZadQiSTajkcrSmILiXm5vc+iEnLIR0SKoysuw9TQaxs8JLwq9+DwuHqxS0CSc3pXG4N230O2yReKUoKNQpMpwqyxvXQSUmKNzhkXdDNFuUypqRAJBRxEsOPxdzqhBQQcXBwUMkpDLHB98+DTKJt0F7FxaLyUYiqnlYs5BnmuYvQ00EHTg5znoTk6xDYwDqGUxgxoWlYrsgrcdhBz9oXsDuWcd3UAYvq/5riChg4G895uTtZV0Wfew6lM1H5xX56G7seUlvkcXFKfaWxF0JH8hbaeLIqjmQYcRXO8/EHSL9CjQoUhovEqZqvdmeHLMiznoRCv1AEYSEtV9EHPksYOa47rwCobk8AlrVFfuHf7VRGuFY3fzb9GTbKIz3NmpSLpeYZJfbgEfBZlTOFAi9AokehPJiQa9gpzc0pfqjMdErMu0ZMfgQTdhHdYYu/Zu58wIemPhCm1/63PC59YI7QN9fbT54joBCTiFl49Vet3TUvjuETegg7fW7sA+o7iwbmHRZe4ZVgtEElnmETkeluReexaVM6Juxs8Qr2gyVgm6dwsJhkmcc1Dag/7IvbtYzNgeIYnsqVdA/9nTK2gILIZZOOYxIitJp7C0UBwH6wGQo+ccHYroMe/04Thj5KjKemtBvG+GIe5IEYXDiFFbiESIkV8xojCuS+ZKK+gDbuT9q7sn04m3qx/k7cohkAl6OdwelL3SHHQ/YeoJ227eeXqxvgB+15552TvdUqXLhbJ6DzXExkPc4Z1g8asQumpEkKRd/bHmOYd3dSgrvYEec0YterD2obf1QA5vwE8MbwuJOBQrhDVetrhgRIzfs8AVLb+ReHleJZV6UHSQO3hf7tvYkHvWNrTKJQQbzg0lbbZRlX2zzGtE6x/kop/oog1PVb5rYUqV6b++q5xX6TWPvExDr1MLqS+yJL7B6G94poSHIl0JulbMRd41vXbuucU7fkeUAp7JSrenCvvSVEuxOdZpqzCmJ7cis+jpPJav6tK3sNgEZ4STdqsC7YupkbqU5EcCx2fQ6Q/lv35t8kqfKUFHUUDVT/wbFwAAIABJREFUTazyDnqfwwhxX7sra/2+LHf7OmbmQcYR0t6oyxysxNWqtKwKrVvp0xxeXB/Vxbiq+nMJBo2kunXRq0xiwIr4USHzozmx0/BjLKTuzVNSYkRdC7VEahvNBbye9W5fbvjLD5dSWJWgr1Y1p1dzatNiNMGDGDWLyM83a6EFYm8fCmF5OucnexUJuuci8hipQsqQS/veThFkSypkOEC0tgZC3JGDfvvt3y6F3UVLewTdIDAGnPiyojqjf3DvWncDXqOAJTF2hYu8hvt7Kx56U7hd6iF3Y48TaYxlzGkoW9jDCbqf38cW5gWLZ1r1djMmeFtEz0H3EEim+sTwaVyik7yU7MEL89UHJvOM+Hzdc+5+TQvCCNeQdj0Jeihb8VArAAcch1oQrarF4XpadCNWsPZZ4TjSW570sbYiR1ogUok7f2ckDyvgx9QbFoXDc0OEDSOvaMzFdsAQnnUYssw3b9XZq9oRgh50kT0g8ygOJ4iuGsrBtQ4LX+lzxThFatRA3n9nOYJ+bvUp0lSCHg06NLbAqGFGDw9yD6k5nBRTUlcPOpVt5OsO1X9O2WQEwMaqK+TjMzaVgrwjjj/P1/Ua7i4vjT4FEucywXPmaWCpKTlfGaENHK7JzFbWlyykIGj0UFcOlqzifvN/ENFMtOSmXCadlKQnHutT8SrqFyeRby4Pg1yMMvVUEjE9XLrmFgwGyQF8+0CMrb0eSCe6577kdeU96K3Zy5Sgk2DyaobaU9yXRo+I5AXpCLMFg/ofrWZaxQDRL6pb8C9E9tkcwW6hLo6tGvT5xO47kGc4x2BETQdGL3r2eW1u5tb1UqNrWEFeNU+Tb16jCONLx3CIuvG1hA91OOzLcudoqXXi4oUFy4+PYfhBl0vGg86LRMfDvxmlyCijtOYIvmPdk2gM5ppDCwMM0PCQ4wum+Yisdruq9+F7vEO39TUFuyEUHcZNhrizkjscGdgWst5Tn7C+oEaHjrHEwUVZCcdZfJGgD+Sv75ks+uBUcyL/duYIZIJ+5hju2BFOnDhROHav15O///u/l9e85jXy+te/Xp785CdPfG5XvK46Z0H2TjdptTMyDGXPCTAktBfD0iqqCWlhWCgLfShB1z61FSVXh9tdFXLwcroy0u4P5L4NkkosHBBQEEb4u3xxXvtWw1vqBB035UTHz+sLqioVaD0xGMhafyj3b7Tl22sbagiAgFJPCXKWmzU5dwaZjsiJhEdWZBkEvVaVyxaaKkw/863JrIWO3a9djWIiRaOFPwh6ZmN1U+ipDNuMYYD4N8ME/Y8eI+JvxfZggEAu5xAW4Z7e1yKK26HAXhc+dyp5mr+pPZ2Li65fT4HIjRF0D5HltYVVwxb2uHjbKq5vuKa33namBJ0lX1x5QM974HG8i37oyLVC/+GhzDQQulXTUPdpKNMaIsvK6cE8op5EX6RZAb7wig67eBtJpftCuKJZqIuhzNEaTbJQVU+e5jHrtbAYk15RuA5//lTDXNE9Y4KOPugg6GN90OlNs+c1pmBujtmM6FCBG3vO5knxezmZgCnot2kop03UVAEmKSt6ShOeTiU83SgZvAjP3lhvS/8MPOgg6NoNwgqFabi2FiqKeebuuU2/AzRQrjyyB94JV8BwiU74VWkMvcg5ltVYZEXflKCbJxvbsUhSDBl3rFC0U8m5zWsNd8e8tmvA8XgfMVzex2qqNKZjTmkZCPrhe0sprXuVoLMtHHPPARplv3f5qFs7TiXolaqS8z4YgRF0ksJopHTDhcp2DS+JXjp4h/GNE3Qfo95NAsZXT78JHnQzkGA/Rn2xTSTmSdGDToLOHPSK7K4hz7si01KR9f5ADq4aQTdpjaaPUI7f+81yIe77qtcZQU/nHL2YTtCZg05DdxrNgsJwTTVG0k/uhlxdHzzm24xLW1Xd4BiILxq7jZL7HA30l/KDJbk8Mo6GgXgM1vdAyH1HerI62lCjQVtJF1MbaKyiOYJtVntyYPChUuNOCTpruYZXNH0Wvw/iwmXgyYRWkDOnIOjjcnT84Kc4to3kuM74sU5iDPCvMVWcO+O9vS7y4t8oT9Cn5i+XRqURjI8qq4ygu1ynDhefrqf4sAaR551QVtH4j3empmknCYviUc1BCTn+x/08ksjbv5KgQ79B2sNQSWmx7oh57c04xrZrVsvActNpRLBkjsSAHKA1g4ES9PaRUmPuwnkQdDMahPojJp5cdzJDRnHt5DpKMs55Eom9fQ71TtyICtxoNF5HdUCVXTXqtvCYI3Ks31PZ7V2VPJzEjc27p2as5VpN5Z23HtY1CnMVKYIoFmgaaZC7auhLCLrpPSDon75nMuPGqaZa/u3MEcgE/cwxfNCPgOJxr3zlK+VLX/rSxOd2kvl95y/KPiPomnNukg5vGqJkOcgqWNECxApQBC+UKQck6KgkXFVyjnxi7IOibxDMc/W6Fv45tL6uyqV7nKbQcq1Wk0sX5mQOocvNhhW6oCaXVktVuhMYA5dp9NE+2ulp/vk96MWi5JYEHR4fVHA/bw7h9CJrfVgHEeJeVc/3RbNax1Q+f2AyYeTY3fiIi1kEz1iG3lVqUU2eCuMAorrk1l8q7FjSPGqAVYpJ0Kncw7ABonq809HFaaHFon5cQGNFal9ci7pE9KwFr5ptQHWQ5EPzN5M/V8qK3mX32MIzNpKbvz55K47oQX+ittVLX+4ZYQ6+tZ0bWW5rUp3YvRE02hBVWoeTnHHTOZiVGV++vadreA65e1D9d49iiFjGYn+6dnuuMsY9v7BqyXGIxhBBV5J5Uev9vvzMh8p50FHF/e7gQU9D79z74Vb5qOxx2G02WBTHSdyP33P7MN189y2UzPQrs0tFJj4e/hnSBvypRSW74M1KP6gHHVXc2Qe9bIg7CLoTRCfmUHrSfHMvCjeuuCIiB/OEBdqGVpDQooOUbKNQmed7chRBUYKcdM85FFqGHxIEyorYytBTVhDGHUi5HZd90KMhgeMtaXGUePz5/HgNKXGHLLntSDmCvu+8izTEnaTSPdqUEp6L7t/TrY21gOGtJOCQV9bz2WWddveIxZB4LCOEVjFeMdRII6s+baHsiLxSD5EZCyCHgZmHgGrdFGxjhRQdE09VQEY4zJwzVdEQd+SkL1SrDHE3gq4xMQifr6JbyUBu+vpkXStc1u2t/pASJSdfuBb3iKMAHOt+8L5p8I6EXIt3GjnXbhFmRNVxZBPYZbWn64zN9E26gcs4n+e+Pf30VW2hhv/gJeeVcZx6/3OQ8+lKU1u8raPV26gvK6M2n5O3ajRSD1oAgv6twQdLkaWbfwMV/rcm4idVeuyGtuLD/p3XyQj1Mk5ysE1YbgHu6fD23091rtCi0CUvIv7aIi/+zfIEfW7p4TJbnwp1ZnAdGtCCWkMmI5RE6hOmnoDQbswr1iCy6EUzJsILi7+pGiIS2WHGoyMpcZxw8+iub/QsKsk9xp0h4y3Q6QGY0JCJwpAgpNbcXkk6CbqOv9Cirbg2uU5VSFHSiJy+LG+Uc7ycPzcfi1DaWqhaRCFaLc4eWynDMuuk3c1a6WemxHE+qdxUmc5oBRBx3I+nm3ZDu2OuG6FLjxkpkEqKOTfXQAcbFIyzSEgl2ijKTMI+XWvIXJMRPH5NXILggY+xhr6eoKDzxw9OVpfppHMx/3BWEMgE/azA+OAe5Gtf+5o89rGPldXV1YlP7MrDvzp/Sc6ZaapHUkOaVEQYGTHPklrmrR1QmpdrXwfVAt7z/qAi97c7cvuJFf0e5BzKE8g3SOZ96xshfBhCFQXqEL58xeKCbrMIgu5F4iy3GELNvVHmx3RKpsaAI52u3L8OD/q6kWWSVrT/WWzV5cKFac2LX+3BCimy0mNV8IvmUKpnKJ+6q5wgf9lVF+u16uJkZNE9WL6MOHmnV8IVDZJxb69GwRvD+mNoLMPd0ZcyhrijvzujD3yZZUVqC1HzkZAuJgXDgYW1J4WxXGl1gs57MKQDsYsWZZyif4YE/R1PeYJ6/F1xxrsTdE+loEeGCxjxTKxHhrd+E75mblaq7IQc3DBDTHlwBTKEnTmJjXmxxDeqX06ogndVw+y0Ulb0npuXgk/HPOgJy8XRzoSgo0jc3SvuQTfePUa+gzfcxkRBONjthLsKn4tqpqpsQbM8uXjZKkSUJD0aTlILiT+vTUaTJDInHjOo0+ptYZu1gdx+e7k2axcu7QkRQO4hIYlOIgjSoolGgnE9Hl4OpQlEGnni8HS7YQjjDN/5eMb36oVCBIzV9GBdA0YnUXZyjjEUssLuGUN0zSCx9GgYekI8NJL7pKHr6dOJocWcFrg/zh1WcS9bJO5cJeiMhgIp9mtQSmck2MmypqwkxeJ0Tmq9EsgzC01V4k5yjv9w30pQQwEq+oNZUC+2qqPnnC3ZtD2k5aNDngMvkAqcT6u6g+xzeoZZ7IYPbV0mIw1zX0L7xmpFlmo1bYF476pHedE4OF2DQXIg/+22cgR9T/VJgaD7s/I+1CwWF2mwk2SN0DFyTpKOMeD9zK3NlYUH+zQNHvSxTgz8PdKFoqnYf4158CDnMIvQp28E3bDFbyhW16o0pK/58F0tXKcEHQYYE8dKwJB6gBV21JM7Bh8oR9BfJ2LdUMMw1ys+DSt2yVEg6Za3nJLz0xH0lJSdUo6eXETGqKatLAbJfu5BdwM0Qtxf/B/KE/T5XVfJTL1lBTBJwkGWY9KgF8fkb3gxUogtG4NTwQg6025I0LX4pcozRPPQkx5VD6YIeQqQyzyQQRyzY23DkLqBc/masDFASPdAhtpW1srfm+dcUyy95obJTjVA+hqtRSfjUx8N+7K6MRnJdJ04JejpUOO4c10kzpt0LCrhTnTowv6uUxX0azrCUoLuhjaNvEoisGh8TKrfW5SR5p7DsJIQdOAHQy/WJPw+1+A40Fz0JIoNufomHvR7bZ83GMonDk6mE59i+OefzgICmaCfBRB36hBf+cpXCoeGcDx06JC84Q1vkH6/L5/5zGcmPrULo+8/b5ecM4MeikaGVAGj8uNVqp38URBZrHDoHc18TCgUyD1HWPKh9Q259diybovCR5qLXkehNvZyZAs1hpfunmpqjvE1e3fLQrOpIfFQRp0MedENr2jOc/nqPFLCf996W+5ZW5cDq6tBKYaCNov+2NNTcuWeBSXoJ7rwfo3kWGegue+Xzk8pbn86Yc9Hx+7F332RKoKqYqahuyboVMH0MFcv7JH0Og/V2m1nL6aSkk4VpnYMLtwjRhVoSFm64sdFNhR3s4Ug9erj3+6RCcqqhdAyPiuyMg8lCxI8MQLgedw8IW44Tij+c93jQ66VEzhNAwh/0d6risIWTNDJkQ+HNP1CM9sshH2ryaE1Aqy1HIe1GyS4tRtT/Hp8oS1EFug4pYKtT8QL11iYndZwwo4x6U8/o5f2DR/6SCmF9fnXfpfcuSyy0bMq04WcQp4qXGOqHdgqnBLzkym6W+q+J1OIg3Ekzkk91SmU0fgT541vHv/tbN0MJNbDu7OBNmsDueOOe0phd8HibsXGx40qeJZOwqrfvAd874WLPFzQxwhkF0igh3xyynDscH96SPQ4Jge8oJvLgpRIpY8I22GbQNytQ4CTBE9FcRmoRsgq80E959BJPbZR732f3Yr5eSh3HTtcCrvzjKBjniCU3e+XHiHes2NIPExV1bQnRsOAnOOP2Pi7FdArKPgeHpp4fIy14P4AsxoJbH5rbroRdUZDwEDL61LZzEkYjAqaqgDDxXDIXujwpNeqshdG5P5Ajm60rQo+j4PaF8D3LV8tR9CXKj8odXjQccGhvzPDX2NoO+8VtVkiMSYOJLpN9Vq7PALV6ahyXTR14VbdG++GVv/sheiiPS+Sdu5HMs7qLiTY/h9jzfifF6zzYHkn6Cwa58Vb+7q+L1RQL6UvX+q9u9S4e9trzYOe3GYwQG6Sb4ksSdaScfkfVrikkNxp+H7sTGGBZuPHHAv4MWkbVs74+WRyMa0yb0JICfpvlSfoC+dcI40axh3HBOZGu98xz7dVTHfDjekSGG+x5Wo0zrjxDMZDtJFknjdznUGsgZ8Wu9R6ECTeODeOgHGAsXK0s6bkfAMEXUaytzWn3l2Qfmy/0murF7mH382b3h/2ZaqK3Oq69gPfGPR4xaaGes0QTQv0wnOqCAyk3y4n6y6cn4stUu1BV7awVhcjUeKIGB9LUTMb2wZyVNcPEnQYcnnM8VKLXCedoCcamq03DPsn8+Yx/XmApAO72UaTKT8W6u4WLhbyS9YsM+p++uBkdZnG50P+fHYRyAT97OJ5Vo+G3ENMwHGS8rjHPU7e+ta3ypVXXjnx+ZwoXXvhPjl3dlqFq6/1IOtpIR66ZykWovAx74cKBC7t6GWNPGl4sr9y9Lhu7YrsTK2mAgZV2zXCfURP0bnT0zLfbMij9u6RhWaDlsAQBmRepgrbXsXiTlHk3asEfUMOKUFfC1ZWLBJK0Gem5Ko9i0rQj3f6StDxDmKNKu5w7bzna3eUUh5eeMWFxC0tuJN4dun5scJvKUEP3nMrKuUkJSzeqTU46mBUeSnQKYSLq71/coLgYdweWqb7O0E3Up6SdJLMWCjGiYIFYul9unEEBP3tZ0DQb3KCntyCq5usdp4UtrHF1y2/PtjHzROm1wQlNiXoror6viToTqrMv5R42pxsFwl6JPGquJp7jh7DqBwGz4MN0xj1wW3OhKC/6IcvlzuOoSPBiK2qfFAkHm8+580qZ/gmuc84ocfn92lEih/sZArnSb7n10XVZpyYpwTeJY62aux0laDf/s1yYdoIcdcxbvUdqNLxpV5rI7VmFlBFEB4dpuTE63aPuhcB8gq5BeLge4RWkfQC+7F9rKbjxosAubHI528Ys5aH7qHuuGav2huKApk3HkZReJ83tHsGxze8KncdL6e0nn/ehVKt1XVceSi7kmWPBNACbjRyuEeYqOE62EoNeeVK0EOXBRpi1GtjGBOfmL+pgf1JtBDbP0YvvofXt8yo7MZS78vukUuBoLsMg0xBqgLIeQWh7lXZXa9ptNKJNj3o+IOhehYEfTCQd5Tsg75QeRwJejAPMYdW78MKwHmQe5GgW1s6qcpUpSUzlalgXGJhNnokWcyVxBqvUFLOOxXY2ZSg23c+21NlPwY7e/SPS6609aAZp6wkGM6P61gbdXV8a6vQEa6tr9exoEaFvny++8el1titCDrXBs6KgpSzD9FYPmYnTPcZa312SoLu65GdULdNd0iif9LfIrbpCnpyuVoQmSOR9TMk6PPnXCM1zFk3OKLavuYzI2SahpQY00eJpBExOscrgugOd864sV5b52rBMSPoI1QX79Fopm0UGUmE90a1qcdy3XK531Z5qkUORWSpOaPeeDhxqD92rHI5vejdQU9JOc6H7RCxARLLuheM+EzDwDn3eU8jFCVeL7dOXLY0H0PBzfkSH3iiAesD22q1Kj5jX/Oi/mKSUQ8FBxdlTXvgyNh4GRvPmlIQakOwDo47x9QYmVbe98KmFRTKNIJuxeKCLmT58ox2iPeF83zywGTYnUZbyD+fIQKZoJ8hgDu5+7e+9a3C4UHYzznnHJmaoge4zMsJ+kuuukIuW1pUb6bnK3o/W7dU+vLiCq1XpVYvo/XbhYBAjjQI853LK/K5++4nEbe2POhvDoHa7vdCmCcse1csLcpSqylX79ktc82GwFI5vgB6bkyoYJlYvu9dX1eC/sAGSbov2wg/BfHfCw/67kUtNnRMCTqMCKzy/rDZab3+P7q1XPGfZ116AQtHKUH3tlxu7eQiBPU4FkayEGxrtzauSERikAho7QnqL5Yhg4LsRXhUxI8xVdq9ozMcfXxJ7M3bZZ5jKtO0rut9bGJwqQeLz9I9i1Bk/+j28kXibrruCUou0iIxmntfKPrMG3MvZxr6r1smRqPxkF/F0noek0xHiq7EPPj4iLWrKikEJAY2onx/D4G2Y3KJTXoFexil4rp12ab1Xk9++i/LtVn71z96mdxyqCPLbYQVhuFeVFT165iTfjL5sEnR5M1M9jqZZnuK4xR/CurLqb3umr8Mz8pQvvLNcjUjHrHnYRriCeIKjwtOCO+Njh17bqyMy7xHvDQX04yDMLZ54R3tEmH7evVwhAa6rHKPU1BQUVjRqvC614dFf8Zv26NxGPat+er28jB7Vxa1cGIdgdqcI8yDt6rgFqaK/HdVWjVEFAS9ZOGk8y6UCjxnVoANckPlhw41oqUedITjuzyxWhrqPdcUHswT1CrhnFFyb/fv0QVuC6FxwuetFXlLoxUszB4EnXninhIQx5OGzpsBwPxr4ZxKREYoDicyjXuqVGRe07i4RuFZIhXFU5cQxvuxA+UMudOVxyhBZ5wN/wsEHf5ohPqaL52E3UPL0Q6uoX9zlWlZqMyGEm9ORpAHvjZaV+N310pjIkc87ULQsJgpnCf6xN1jHivL+6N0z3kyMwNhiaOV1bfx/Jh7TpKB7GI1uIz6er8zFTSp68nHO+84ewR9K3JOkRfG07gx95Qy8BQSbzwE3r2TJmI3yyw3fqZGUBvnpw2nH7uOtTMMcZ/a+wgt1khDEMcczURIu0FPbeYgO6nVWZlUS8c+jF6LugPXB9b50Wrslu/sBjBdW73rRAXjEF5bEnYn49NNdA2q67VpoVyTAZBPWjxT009EVvtou9phFKYa//inYdu1ujpt2COcnXtwDZ5kNOj35NvHvlFqzD36nF3s0GKRi6G4r8ktfUyOw6YYlqiQ6QpjDhqtjWSfIWN0HLnX24xaiB5QHdz1GifoNpg9fcfbXE7XG4pFs1ZXI4jrR55S4GsReqNjnWCBP29UF81HwBT1gPx6gOtH7posjWwypSFvPSkCmaBPitiDsP3nPvc5OXLkiDzjGc8IZ7v55pvlta99raytrcmznvUs+Z3f+R1ptdAgZrIXKsMvLS0JCPqliwsaBlMk6LbiuUqTVMj1ip2qvFhIJhTRY+2u3Le+LneurMjnlaBDRDB/EnkwEOpQfjz3EN+BoKNFxJW7d8lc3RueRgu5C0l6ClLPG9nc/evrWsH9yEZbW63xqiuaH4qc9j3TU3LFrgUl6MfbIOhDWe6RoO+bnlKl9b233S7Hjx+XxcXFbYHo2D39ooepQur34/mUwIWKoSsuSch0QkJd9U7fXWl1ZUk/J9XK8VmrdydG/BBZYTLXSbTTQ3rQPRXBFNmEpLuF/WQEPVIEV++Y2/reb949EW64J8fu9679AS2qlRJ05uWPt05xo4a9JxEKJJjEYjz3FtesnoBQdKpI0F1B9mVqzMYRDOZFgh5JbwzjtUJYlnNuNimrNFvsg+qDCx70X/zoJyfCznH7padeIrfe2zaCboOpQJLjh/Trk93f+P1vhUdUSJLpMUbMN/P0rZn72AwuzjdTejZPQsYzwvMDT+s/3XW8FHZX7T5XutZHlt4Ia9uVpCcgv9yrF+MOWBjTe8+ytgXbn9EzROMWlSN6prw3ORRVy5fW3HRWPVYSY4XR4P0hefasRd65e0KY1hMb3THaJY4pzB/Uo3DFj+10igS9o/mckaDffeJYKez2n3uBiBbB86J2rogbMVIPGvu4Fwk6C2CiPglrS1ihM/dgqueHXnTKTY5U5mADGm8lRmw8GogV3FlEVEPAa9FgwKJLXo08FqGi94lTW5+bEXSsntoXHWscIjVA0KH891Bt2ojncCifPXRXKexa8qiQgx7ljhk1pEHiof5KVl0O1dyBdaWh+d6zgaB73i1NDt0RK6mDaHWkr2tfA8QomJhAyuihTAk6UMC3YTO1aBr2Zj7wcNsxP7XTqUDQaeqKefH0qjtBZw76J7vvKoXd7/6aSCuqBQXRsEnCPNgEfSttYQuCHqjQhMbPtbbIy94kE+GWrrHNXd/NDiPQGVwWWTgzvN7wSLuxMFSAsQrqkDUYRa53ef6yyiYl0pGgO8H3+/QiaNUKi9k2VC6gUDCIeU3mWtP6jtGqskENaWYiR2QliGQFtYK6stpr67aaGw8yqn8Is28oMV9Vgj5ULz5e2A+CpD/oycFjd0yEna+xj967S2tXMLondi4KKXTJc7SrNrkdBwS/5wxyvVpxSvqdU2dRGq16KNqpuawO1iYuCOE4jN6ibjNbb2qeOSq0493XDV2LrNYJ3jUCQQk69SE3E/Jquabgd0bjMV3qY9+6ZyLstqU4541KI5AJemnodm7Hpz/96XLttdfKq1/9aj3JP/7jP8pjHvMYueGGG+Sqq66SN7/5zfLyl79cXve61018EQcPHpQLL7xw4v3+R93hwIEDsn///m3dXsYuwjQJbtgrY1cOu4xbcWpOMu4ydhm7bQn2bWyUx902QDrJJhm7cthNglteY7OsKzfKNu816bg7W+fNx9mMQCboD8FRcd5558kHPvABrdSO16//+q8LWqt5Ubj3vOc96k2/9dZbJ756FPi45557ZH5+fst81YkP+B26A6yOKysrcv7552tY03ZeGTt6+SbFDdhm7Mphl3HjzCwz7jJ2GbvtyPVTbZPHXXkEM3blsCuDW15js6wrN9riXmXH3ZmeN+9/cgQyQX8Ijg7kmN9+++3B0/34xz9e4FUHUcfrrrvukmuuuUaJUn5lBDICGYGMQEYgI5ARyAhkBDICGYGMwP8YCGSC/hB8jhdffLG8/e1vlyc+8YnS7XY1Zxwe9Sc/+cl6tQh5f9KTniRHjx59CF59vqSMQEYgI5ARyAhkBDICGYGMQEYgI5ARKINAJuhlUNvhfV7xilfIl7/8ZXnjG98o73//++Wmm27SsPRmE81hRN75znfKb//2b8vf/u3f7vCV5MNnBDICGYGMQEYgI5ARyAhkBDICGYGMwIOFQCboDxbSE5zn8OHD8pznPEdzzufm5pSgP/vZzw5HgCcdvdBf//rXT3DUvGlGICOQEcgIZAQyAhmBjEBGICOQEcgIPJQRyAT9Ifx00P4BBL2mbW7iC6Ht+N496g+7AOe3AAAgAElEQVThW8iXlhHICGQEMgIZgYxARiAjkBHICGQEMgLbRCAT9G0ClTfLCGQEMgIZgYxARiAjkBHICGQEMgIZgYzATiKQCfpOopuPnRHICGQEMgIZgYxARiAjkBHICGQEMgIZgW0ikAn6NoHKm2UEMgIZgYxARiAjkBHICGQEMgIZgYxARmAnEcgEfSfRzcfOCGQEMgIZgYxARiAjkBHICGQEMgIZgYzANhHIBH2bQOXNMgIZgYxARiAjkBHICGQEMgIZgYxARiAjsJMIZIK+k+jmY2cEMgIZgYxARiAjkBHICGQEMgIZgYxARmCbCGSCvk2g8mYZgYxARiAjkBHICGQEMgIZgYxARiAjkBHYSQQyQd9JdPOxMwIZgYxARiAjkBHICGQEMgIZgYxARiAjsE0EMkHfJlB5s4xARiAjkBHICGQEMgIZgYxARiAjkBHICOwkApmg7yS6+dgZgYxARiAjkBHICGQEMgIZgYxARiAjkBHYJgKZoG8TqLxZRiAjkBHICGQEMgIZgYxARiAjkBHICGQEdhKBTNB3Et187IxARiAjkBHICGQEMgIZgYxARiAjkBHICGwTgUzQtwlU3iwjkBHICGQEMgIZgYxARiAjkBHICGQEMgI7iUAm6DuJbj52RiAjkBHICGQEMgIZgYxARiAjkBHICGQEtolAJujbBCpvlhHICGQEMgIZgYxARiAjkBHICGQEMgIZgZ1EIBP0nUQ3HzsjkBHICGQEMgIZgYxARiAjkBHICGQEMgLbRCAT9G0ClTfLCGQEMgIZgYxARiAjkBHICGQEMgIZgYzATiKQCfpOopuPnRHICGQEMgIZgYxARiAjkBHICGQEMgIZgW0ikAn6NoHKm2UEMgIZgYxARiAjkBHICGQEMgIZgYxARmAnEcgEfSfRzcfOCGQEMgIZgYxARiAjkBHICGQEMgIZgYzANhHIBH2bQOXNMgIZgYxARiAjkBHICGQEMgIZgYxARiAjsJMIZIK+k+jmY2cEMgIZgYxARiAjkBHICGQEMgIZgYxARmCbCGSCvk2g8mYZgYxARiAjkBHICGQEMgIZgYxARiAjkBHYSQQyQd9JdPOxMwIZgYxARiAjkBHICGQEMgIZgYxARiAjsE0EMkHfJlB5s4xARiAjkBHICGQEMgIZgYxARiAjkBHICOwkApmg7yS6+dgZgYxARiAjkBHICGQEMgIZgYxARiAjkBHYJgKZoG8TqLxZRiAjkBHICGQEMgIZgYxARiAjkBHICGQEdhKBTNB3Et187IxARiAjkBHICGQEMgIZgYxARiAjkBHICGwTgUzQtwlU3iwjkBHICGQEMgIZgYxARiAjkBHICGQEMgI7iUAm6DuJbj52RiAjkBHICGQEMgIZgYxARiAjkBHICGQEtolAJujbBCpvlhHICGQEMgIZgYxARiAjkBHICGQEMgIZgZ1EIBP0nUQ3HzsjkBHICGQEMgIZgYxARiAjkBHICGQEMgLbRCAT9G0ClTfLCGQEMgIZgYxARiAjkBHICGQEMgIZgYzATiKQCfpOopuPnRHICGQEMgIZgYxARiAjkBHICGQEMgIZgW0ikAn6NoHKm2UEMgIZgYxARiAjkBHICGQEMgIZgYxARmAnEcgEfSfRfQgeezgcyj333CPz8/NSqVQeglf44FzSaDSSlZUVOf/886VarW7rpBk7kTK4AdyMXTnsMm6cmmXGXcYuY7ctwX6KjfK4K49gxq4cdmVwy2tslnXlRlvcq+y4O9Pz5v1PjkAm6P/CRsfBgwflwgsv/Bd21ye/3QMHDsj+/fu3hUfGLsI0CW7YK2NXDruMW3FqTjLuMnYZu20J9m1slMfdNkA6ySYZu3LYTYJbXmOzrCs3yjbvNem4O1vnzcfZjEAm6P/CRsWJEydkaWlJ7v75n5f5ZnPbdw9f+2jbW5fY0A4OKx7+2ZeRDEWkIRWpSUVGFXw7Ekm8/hWJEQB+bdhnIKK/wC8+GsF7O5LucChH212pVkTmG3VZ6/Xk6pvfJsePH5fFxcVtXbBjd9fPv0AWGzN2FpyJ16H/X8HZhzIc9WU0Guhd4J7wHREciIyGgrvkncZXRbfDdfN9OBrymKORRjtUKjWe5bSRDzzyqILzj8SPi/fKUGQwHMlwwOvCObhxvBacq4q/akXqVV7NUKGvyEp/KJe+4+MT4YZbcOze+owfk3qtocfrj3DqSkAhIqSoSR/XKSNer14escYzVBhsT8CBZ40/jBeFrFKVwUikO+JWfR1D2EZ3tGfFR4Lf/SnW9N7xPa5G9N/4WKvye8XFnhC20P0qI6lXRGYqFWlVRBZrNanj34jMqIz0OnDu1W5Pnv6ud0+EneP2hZ/6GVkbiPSGQ8E1FoeAz4PNM9R/SUbozs7jsZnEK3KE+SOuPQ63La7ZZYFt26yKbPS78pR3/2Ep7H7j2qdLs1or3LfLGb86v2yMIZ1rNqMxf4Ymk/Siw298+Dru9HlgXES0cY9RMmwtXjyCSW93JDLQ85hUGD+XHSLKYZcexDZer20YYB1Ju9+T137qL0thd8Elj5X1gXAuYi6ZGNZ5oxN2pDICsiIArPduV2rzS+WQY2S/cwyPFDfMH4zrqRpxhKTDbzYNI7IGMbZX/FwY2Fxs1avSrFakJyI9qcgA8m4k0qrWZLpW1XlZr1WlWavKTL0urVpdFqcaUqtWpVmv85x2k5B83X5P/ssn3lMKu9c97cdkptnU8Y5783Wi8M5Fg7IljCWXdZwrOr7wn016H2VBlgVhkM5yjgN/XPgXJVpB1DvKQQ6mo8pHrY5JPMeRyTLIZIxVrK0mQH0++xjGnNno9eQ//uWflcLu719wrcw263rOsMqfMuDPxh+m6Pik3mL6helh8zTOXZNRW0/Z8O2wMlKZPr6Gn2a35GfOEF+30tGx3O3LNe/65ES44cC+VnzipT8j9UZTx31/VFF9CM8Fz7+naz6fn6+32LeBeYE/rHU2DsOUNrCCdLN/uNzy8dEfDfWYHRtkGBvQOXQNrfAcPrdx9yprTXppDKMdUMeaCROVbrY/11rbkBvp8VPtarXbkWe+648mws5x+8mrv0dlA/Ui3jRkW7q6dgcD1Ufi3HD1KY6EdPvOYCDrvZ7eKGQA1qFdU02d03jhUD3oYjoviwM8XXc4jymbfH3yuafSIhG/OLbqCCYzOG/5HPBsd0+15BG7d0mrVpGZhklana99+cWPfmwi7LY/3vOWZRDIBL0Mat/B+ywvLyshPX7jjbLQao2pzvHGiir16b8vC4mLOhdNqggOR9IeDVWozNRq0rQQdJJ0X0BTes7vEt1RP+OYEIAQqGv9gRxYa+t3u1t1FZpX/rc/0EVtYWFhW5fv2B278aWy0JpW1kU5S2VUZXqlr2R8AIIOoj4kGa9QmyXZtisdVxKIARc1rj8kz74wVStYPrH3KTUVPb9iUenpMSJB54KGaxoOSM6B9QggqdEgcikaBEDSq7ZA4nNV1gYj2fOWD0+EGw7r2P35854tjXqTCxMUPCXRfAdCipItNjCq9E0pxGJVh+JuBBqL0AjXbMo03qnoczXHtQInJeZ6XKPzBl1FFXsqq/q7HisaRxRpXVSNlBsXgLGICrOPOeKI7WDuwnjd12xKvVqRaTVujKRrLGOt35fHvu0dE2HnuP3+M14sd6xXZK0/kgYIkRsZEnrmg97V9FSh8ivWZ5ws5lsP/HEDwOmnRyDiCd+OClzcP5hjAnHz0WrzmpqKzwSpylBa1Y70+h1546f/v1LYvfm6Z0qzRkXEXzxNVKp4rUYi7fli/OPZQn749oG8h22cQPFd4R0jUidDz0WHk14nN8FolBoDnP1umvp8sjxWMi6DUUFkvd+TV330z0phd9Fl/5NsDEhyQdDVkGCiTGWHiEzBIFWFQYwKpM7ZoSnmwSBm16lKOrECyZ4y+RLmlMl4FaU6HykQfLw7Zq1albKgBvnkFp+RTNexXlSkPcK8E+kMRtIZjqRRqSpBwNwBOZ+u12Wp1ZSpek0WW02BPKhBOVeDIIyoUGZH0ul35f/6+LtLYffGH3+2zDWpjNOo5jPTmQaVaLxgAFTClhh7aHj0e+ec1K3c+OPEvDAmNq+LLqkCQTc576Pf+UZYP20O+yx0wqSE3IxI+m4yG/+grSYSG/wba+yvvr8cdt948XUya+TBE9B8XUhETCpYTAGw+y9slOCeCoBgYC2Y2LchH+1+QdJPKRpP/as+b/0rPrOVbl8ufcdHJxpz6Rr7xZf9olRrTZ2HHTVQjWRD56U7PqKhhWslDdv8EyXpaogek2NqMyyaORVzH1dOKPtGork2c03FV1ibdTwb7nynMY5GcRupgaDTqM45T9niTgudC0bQbQPdfaXbkae8/aaJsPM19kWPfLRMN6BlQLfgs4VBHPfl8idZnvRi8Rv/xtYxWwc6/YGs9GAq5D1C3uyZblF2CdYXyCjqXmGNtvt3I1icv4ZnsvanI8x1mfFn54Y01Y+qFdnVasqVuxYEMnSqTgME7mG925eX/9Xk4+702kHeoiwCmaCXRe47dL+HGkE3XSEQnu6AxHx9MNAFZqHeUCVOSUUg6IlSOqZ0+/HCOgwiOBzJan8gd65s6Hn2tBqy0e/JI99WjqAfvfGlsgiCngrVQLrpQY8EHQuL00OQb79CitbUE6oLIl0VwVpKHzOFd9U96JtI+rgiYB56NRZA2bRj2IIGxRokXT3TMCDon/nudKUwghYIOhRhnL+qBPFMCPqHn/8cqddp3cdzAVpd83I5QXeSDsUaY8AXQLfyY2EHIaZFmcTa77FWocKOa8XXIOi4H4tFkKET9ESpxf4DM1DQmEFjgFqcxwi6Ktu2lAYCJ0NVMqDYzCYEHeMW19izY6/3+/J9JQn6G5/6IvnqalVW+iM1WOG6OP7iwu6TKPhTwzZUilzBVTXrFDYeJ3uTiTj3/Ma93LNSOI4peT5i9Zkn0Rv6HO1HPLOqDKQp69Lvd+Ttf/fBUorX/wGCDu9oIP6u8rhCWPR+eAQJcFBPEwxZaaSFk/Pg3UnIVULQ0/umAlZ8RSOPRfokBgPfMu5joCQHSZ9z4ZnbtZLo05P5q2dA0NsDM6DZoBlQpCg2OMdUva7E1wk6lNr+cGienkgsdawGgi7SrFRkpgZjE6N1dL4mBBAY1GAEDSTVPMowCtRrSmpr1ZqSa3Pny3StJg0j6JAf+GsPRjpn8YfrhJcd3vNdrZYeZ0EJuuhxMBb7w4FKWBB0eND/30++t9S4e/NPPFtmmy2de/DQB4LtK0cYR7x/j95wj3mM2HH5b6MiMSAFIZAMrZNNbSfgwT/pRrLEWBa2Sb2Y9m+ScvNYBiOyG5OjR90J/Ua3K7/8p+UI+h1bEHSX3QVS4kJQl1Uj4rrOpXONYysYshIlwbFKDe3JoaKQHVtiuUa5CeNUktLW+S028etxgu6nAEG/7AwI+hde9otSqWGNHUkba+xIZA1z0uYX11kaGdRopMZlEnSsYQ0Yxoygbx5LMSLPb8nlt48dPCeqOfzGXQpO0P2+1didRKn5M4se9LhO4RgaTWdRhW5u1fWaC5aeEx70sgT9hVd/j8w0GtTJbE7AAz4YDs1wlqy1dvNcH6KOEuS26Q0k6F29PqzZMGaCoDu+6qAwgu6+ejd00DAcB55/wnFolCxGcGBLHI9RfunKwaM4QV9qNeThSws0VNaoW0MnhAf9FR/92ESybjIdIW89KQKZoE+K2Hf49ilBn2+1Nt1NYsjctPana9RWSsDpft8KOt/Hz7vW62s4+uF2VzYGAzlvekoWGg21/NGRbovwmMJbFGNRGe4PRdaHIznRHcjXlknQL5huaNjnY29+y0TCyLE78oqXKEG3dUFvS4miLiL0nDO03T28FPi+UIXwT1sgnaRTqJJ08qDu1fZF3n0JRfTH6XkMp+8z5N6ICS3O7jaGIjqUPhagwVD6fW4XjgWPDrxc9ZqS3Ua9xhD33lD2noEH/WPXP0cq1bpa86E0QHkYaBi6h+KJrA4GOgY6ZvGH9wseOvWAmecN70ABSjRtGqBz0erPVRYLfFDBzJNuasTYAHYvkocchvC7CryqDE9zhbmGpc489arQy1AVDZB6eO/mazUlA/AQUtmjeoiF+tp3/GGpMffa614q/7Ral5W+aGgaLfrR85J61tzF5jPFiYGTckUgWOGTkP8tZdspmHxh+6Ky6t6TwthMvLquWuB3JXqmNKuaxVAUVSmGw570Osek12/LJ275y1LYwYMOEpnKmqBImUhRAp4YCtyjm3oGx+Wbe9M5FIqeTr+fzXOTZ05RdcXWbHybmHxQdlNybsKHpNdSNxKDnxMp7LvR68orP1LOg37hJd8vg1E1STGhB10jk8xz1DIPOuYo5slGfyBQbNMIBd6zpc6YgQ0GrMV6XYl2s15Tcr466Gs4/eo/h+XjcdQrSCixd3j6lJRXZK7RULJdUeILYgEvF1JNqAivD4fqRVcP+sCim3A8nKtWk5k6Qk1b+u/ZBvamIQ331OnT49WoiPQHPXn/F/+i1Lj7z896rsw0GOKO+4jjJc5cH2eusPtnN2TgXtwgyOUvyc8pMM4YuTE+jX3YxGlmc9XmY1gSQmwXj+DecsrXGEWi65wdzMmE/h487GYY6nbll95XjqDf+dKnyFwTdDEh23Yjqb+54MMOhqmtaGWcdx6xEnDaQsQhbSmdp+PzuPA5kaXFee2xbozMSV+FR2c/ecj5Srcnl940uSfT9ZNP/NzLpaUh7owicYKO8PYuolsY0xcsFsATSGP9qiNyxEheKtOiowCRaiasbU6P3VqI8HNvu67NOK6Fqjsx17ltip++mcHPiahjTPloTgZPOTNfhka+GY3Fdqvdrjz15reVmq/PvfJqadURQxCWIyXnTO8byyxMUneS5cvCyuOTBkFHOiVeuFfIm4VWIxjjOGccXepmekchMgH3XRw7Lit43hi9Etb5NFUqGXSaXlARjRx6+NJ89KAblhv9vvwvH/3ERNiNy5r8+ewikAn62cXzIX+00xH0rW4gLPA7eHdO0JF/BeXu3o2OwON44eyM7Go2pIGQRg9LHreGF64rqLsq2LrDkawMRnK8N5Bbjm+oArl/uqFK2OPefiYEfcquwokJl72hLn9Gzi1EXemGh6qbJTNVDlzBJ86WA2medA+Ld3XBSVd6y75ARTHuSz38xzHTLKTxm3kYBL3XB0EfSK8H+7otkrYawavTqNfVu6MEXSqy3B/I3j/4yMRC3MedE3QoC6swDKjCgLwvhrxDmTjR70t7MDCCDkKKsNWaknN43aCkt0wR15D2EPpGi7xGW7hvOSXosBRb+H/IX7fFjyYRetoYhueI88GpJx3kQBc5C85Dnjty5TWPFko4QwUR2u5efuNRgaA/+Z3lCPqvPxkEvRkIuio7bqxKyFpqNQohtUlorKtW8HIoTQhKr4+oTV9MNOtTYu7Ks+kR0YPtirQpF8GD7iGUai3BBZKgr68fkW5vQ75w22SpFT7m3nzdT8hUonjZECmEhKfeQSpTNFCReEQlKAXDCZcbDt1o4qGhRSy4ZzQXxSMFZX8LNp8qqR4Dyscdo4iYjlL0EHr0ghP0f1OSoF9w0ffr6OYT4TmR18r5SmwwNzWHu1ZV0rzeH0i7PwieJb9ThmtbZIpUZKpakaVGXY1vTtAx92GcW+6BoINwYy7RMId9cQ6Q7HmsCZr7TrMmc9hHKt8xN2D8a+PPCLqGoA6ZL99E7iVC3EHQq1WZadRVKkDmwEPX6TMxBsY2EPQPf6mcYei/POt5zEF3D7qPKzfMJpE4DHm1+gf2bJkzSkMfCZ3PzRDjXhhUW/DMwpjz55Aab9xTiOfrK4XPVycKHP+ctBxXXhOE707Y9RhGsrA9DEM3/klZgv7UQND9emz5LxCW1Kjmsm2r8KB0ajmxKQq2opYzLhc3B7OPEe5NxgPuwbnq5LKYWxSOYKHyA/titdOXS2/6q/Jr7M++TFpNppFtRdCx5nJAxRQ9zT03cs5KN5bK5TVZgg7DyJKwWoQaCal0S4el5U3bGuljmikuTP0Iz8OjMsZy0Lci6Bo0Y4Z5jl3iDYL+tJIE/flXPdLqUJA044Ae4j5O0MeNXdFxREz995CDbrY1yBvIrtSoMRqlJg/mi1MXM5QTAxC+8vxy6op272pU4XNT95A5ecIYNNmDZwuCHkPcTapXYMjNBH0iZedB2DgT9AcB5IfSKc6EoIcFcAduCOsFFIQH2j0Nbz+41pbV3kAun5+Wc1oNmUbxH4TjmJW/YIEuXA8lqxNZhHgd6Y3kaG8gXz7eUeXt0pm6hi5e+45yBP2wetA9+oBSdACapooMCbp7CD3PysPYKA6TRUlvJIb1etg2vzYJ7Z70JFwM5zT/elD7o4rB8zs5D5ZscxM7dv3BUHpIJcBfD8HmsVARPefwoNctF51HP97tyZ63lFcePvL8Z4tUG6rcr6F432gkxzpdVY5P9EDMh7ICD9xwKBtYIEWUAKjXC96uWk3m6nWNqpiqVfWzYuxukeAVInYxXNRNQFROHIPU84x/Q+mHUhwNIzR/RO95zCf1MHqsr9jXvV2wwZOwc7HWAlqaH9eVx7/9nRMpXj5fX3PdS+SW1YYR9BjinqjtsbiZrf7UwXwmRPcqvOexwF0kegWtq+QcT4PyCl70NBwvhNbGcFkqJJ6KEQn6YNCTNSPon7ttMsOQY/emhKAXCPJYznYsooXnnRD0sfzaqJwadoE3WVSHseUwfZPIlPSZpBAnT4nKmf2fm/9SY4cfI4xh8+DzGKmnM4a4/8pHPlBq3C3ue4zUa3Ujx5QJTuY0RUXzM0kuWxbqDnLOMHf+7oYgkmcarwARjGxziAZBdEy1qjJhpddXTzZqhuB5YI7h+F53QQl6paJebxR0cmW1VSVJB6kGgd9AaPtwKKv9oa4jXosE2+NYGtpuhl+QdfXe93osoIWQVtxPrSLDQU/++ivlxt3/89zrgwddU28CMU8iHvDI1LhAb2IaWgykGZlDoo5IIs5dE2DOWPW5n7xuRFgXbDnxdSN6xTneIkGPYetq2EhC28m/o9HKf3NPevp+JgT9tpf+iKDmitY+MG92IR/f5kekyT76DdBkchWpNHdkpM4WQi4IiM17pfrP1r9GA5zxO9NDfPame8WTO7lkSo9Ir9+Tq98xmVEI1+by7kM/8wsy3WR9Ia94Az0IJnisq2mBuIQDcnzahW+SMSEUnQQ9ylGuhzYEfSSG9ZU6BbenURlzlPPWjVJuBOUSnuIS059o/KFBHyko8PZCDsC45rIa5zrR7ciPlSTov/aDPyBNLWCLwppDlWHfXl0VRHXCKZDOzc0GHw4nl3UeTYJjtPsw4fOF46D4oRvjOFq9EC/HpEq+8GCi/5xzMamdkgxhbO6FJj2moLj+Uz7gGeyZasmj9iypB326YfqTiKz3+vKyj+QQ95Kqz47slgn6jsD60D3oVjnop7varZeV0+21/d/9+FAQDm10NV8cBd2gWH33/LTsm2powRgIlEkJOhS1+3sjOdIdyN8bQb9spi79Xleu+8O3llJaQdBRYI8vqvUh5zzYcrnSQR2lR9a8u07Qg7faVsUQd8fwQWBSDZZUnoNB4K4ExOrSm4tSebXPZIl2wZ4ozCDoSs41xF2ztelBhsccSjmUXSXoPCvUjOPdrux5S/nwuw8/79kyqnmIOwn6vesbGi1xuNPV95XBkLmj5gEAOW9UScxJzuuyq9lUwo5/uxKbkmrHkDnpsZCXBtNbxWS9q8STjKUQldfVqh8Kg1mRPzuGVrq1CqnqKeXBQ56ses4tnE/D8JWg00K+3O3KD9xcrkjcv08Iula6TkzwqSe9EOqeVCO3R8jnaI4cLuCmW43HKY5/3u50HjOQFDwNbm8K4bKu7ButtN/DuK/UZDDoGkFvy2fPhKDXGC6bep63KqpGHbFI0HWWJwWwHIpihEIcS+mzcU+2q28+5qIqn3q+i2q/OSONdDtV5/ZpAaetInCixxOekZ78yl+VI+jTux8trUaDFdYRjo56DjZ+WEMiKuuoiA7yi0rHJOgMg3dvHPb1qsjqodYcdN4LcslB6Ff6CHEfyroXTlIPOeQRiTpIM44DxRxE3IktvPGYb/gd34OQdEYjQUTWCRB0EBTzoON35IIihBpyBYXlcC8o5qRGVs05Rb5oVWAg+sI/TWaQ9DX29573ApltNPWBpXnliodJcn/iTKHhHwkPqZWm3IxYARvERs2uZpnR9J1xObAFc9SvtsgpdxIZ88o5Sv0vJd/uTQ9zweYJCxvGIptni6Df8pKny2BUY1QVVh6kCZjvnCMiEpcomtKbj5W0it9yHgWD7pZybSyNwLYphjKnM3jzQRxDZ1pb2QLwm+eD63O1dUMGPXnMH5Yn6H9xwy9o5EZI80IkoRVbdYLOeL9olAljxElyGqFjxh+mf0UPuq8fPraDXAzPxtLDTGfRKDQYvhKCjmP4OAtWSdes3DtsnyFXQMzxDmM39MHd01Mk+9ZtZrnbkWeUJOhv/OFrpVltqMxahbOgP5CvHzsmJzo4FwphUt4Uvd+8uPT5qgPG0kAgc+B84BxjgVdUTlcs9UCUBmEOJaklPsZ9nfFuNl7rw0ZyGHxIy8BxPS3L9R5qb56iJ7J3qiWP3rtLjZRwfHkkGDzoP//hyXW77aoGebvJEcgEfXLMvqP3eCgTdOQl377WlmPdvhxY7aj341GL03LBTEMWG8wbdMppOsoWz8K3oFhCePvdnaHc3xnI5451tJjXd89UpN/vyQveU67a5+FXvFgLC0XRDE8RlzzLIAqkNvWgmw/FdnOruuWrJ6HtXqk0hllzCaiOQNCx6DFP15WjdGFMl4txjuVVdh07Jx1KRgf0Gqnyo3mesagRFw/eHbzd5940eZ5SyI+7/nlK0OEtQ20AhLMeXFuT5W5PvrG8rMfvwBRRwV9V/1zRwCMBVZ0AACAASURBVOIGLxIKQYGcYzyoJ93IO7xnKHoCRUAt3maxVtJsSkJdww0ZJq85WUauncDGQjWx3RIGXd2IAY6PEHZ467QpE8hK8GTxnMHjZaVfWUxO5HCnLd97U7kQ9yJBt9y9ECdiS3ASys77iUqsE3cdSUFjTUKjT0LQt9D3t5R/rqDoqHYvW/AEG7kM3rckp9XUm5gXinFOX4S2ylMP+mHp9s4SQU+8mHHeRAXLPYSpLywosol3JzVuOM5OmtMQd9d3xwm64+rKnCtR/N6ibNyhZNozz0n5plGqSZpDNAqYFHLjnuaglyfozV2PlkYVXipTKC36Qp+Q9U/ydAuvVA65pK0FjbwVCTrZvRZhUy86JyZaDKnnCt0lNIzeELMc80adBF1rkWDumUdrvtFQ4jqL1mmY/1rFvarkHOTuWLcnxzo9VbbXewONwoJiCoI+H2Q4O30gNJ9EHkXiSIqHw778022TKa0u6/7r819Igp5EsngYKo18FtKeVM12DxyrclAu4w8ybw5pGsBfC+pVZGj5PNFA56OnOEXTKtv4JTXeKEkc84p7HnwsfhXXGhZ19JaAwI3+eCcN/q5VoXtd+aWSIe6fvv5H5OBaX1Z7Q+l5tfREbuE+QvXztJ5GpCtbyinvoOIFzDgWk5cZn2LXD/46nq7i67oHM6iECyxNe6dQV7H0mNRAMn5hLl/YDrAie+oVeclfTO7JDJ1SbvgFmWu2GCmm6VhMHYNIQMofPehmHLDr07i/pJtKmo7i48HdI8WGlV6wjAVftY6NG1qTfHGgiDUT47+FmjJJzQ7PPR/X6fw6PYz7W8vL8nf33qddKUCaHzY7I0/Yf74aDhG9gxcI+nPfcXMpx8v7nvtMmak31bh4+7ETcqTdlr/59iG5f31DlrSgZF1mVd5o5ZvU2W+rWJzP/oxhbESYO4tPjgQ+JqTYsGCbPxumUnmqVwxI4MzWNpZB9qF1JBP1/Ll49AzOBTnGKIO45vrxPK1gV6shV+1eUBkIxxfOgRZ5MH788sc+PRF2W06y/OVZQyAT9LMG5XfGgcoQ9AfrzqCU/dPyuhxWgt5VD/r37Z6RC2easrtZk9kJCLr7pZYHQ/nmxlDu7QzkM0dB0Efy8JmKDPpdueF9b59IGDl24wSd1tKUoHt7Xm8pQoGs4jYymWDDRlg886ipfCtBt5ZfUZGHdZTVhd3uDSFcsNwGhZ0Ex0mnR/N57mDgYr6S2socCo9ZVWVfdbCfVys91unIeW//1ES46cJp7f3++gXXy7AGz8hIw1BB0O9eXZMT3a7ceuyYPNBuSx+5x/jDQqjVoRk7wFBaFmKDFX66Vhco6JpPaiHv8KhDsXaijm0Zbu590tmODUo8Fz0nuxzl2jrKc9uSHrzw4IEUzGlYPQwFyDeHZ8c8WtbKTY0CljsKI4A/BIyO+zfa8sib/mgi7Bw3J+irWiSO1+6v03nQA00PVog0gs6JAo8WdWBT9rfJ0KO31zxwBU96qKhDj1FSAZpnjUW88Cl60EnQV42glw1xf/OTfyIocH6PRUwMySQ3PnrA4v040fFjxAhji5QI6QQALc7NrYqlWRR8iEyhx9RNeJQWqQddz6nGJb8ym+NJJfCApSnb/pRB0F/50XIe9PoSCTp9Qrwrr2BdQ3qJFnD0Pt+8AkgpL7qozzMhoFozQh85CTrmCuWn9QTW+WddFzw6xQq7aQ2IxGkM/KE4k7xWKRPqjLQCOcd6crTTlaPdnqx1+4LiW4jGQSslJejNpp5bQ/Hh6eqzpRI8dLhfyAzU57j9G5ORpUDQr3+hzBlBd3nsSjUJSmzZ6J0D4vjks8b5h/2BzNQg45qi6apVNB8U6W/qj16cyy4flJwlfdBD1e0QoeXeVBoltTo+DKRO5qzVIEind9rQavvWUs/XBxxOu2EYgd/odUoT9I8852ly6/GORj8gzUn7jodZRdP1eKHMk/q0bciZncvCCUJCSwjHTue8r7ReZJRh2HFWu9c4RJclxjtuZVh5vr7vmy7Y8WGz7ehQZKZelf2tqrzyo+XX2D83gs4AC8oS70KhJM7SFoAAiTnrqLBgq/Z9CZEcPkY9LYXGbid/8WbUSGPGGpfvHtLOTihWaBUdGNTAHdutOdMNBhezdWi6iV0fxuItR47Ih+66U4uugaBfurggz77iuzSaBnMazx856M9/ZzmC/okXXa9tEdH27L/fd1juW1+Xj37rgNyzuibnTM9ohXe0ZESLRr/Hcd2Y3uho+IZs0UrwI4bMq+EPUUOWTodx4nqc63+MoInHgMxiBBPeSaaxrWJoDgZcB/qpQ5/qDaBXxVoR4wQdVdyvWJrTCKE5xW2k14boxVd/8m8m0k8eLG7wL/U8maD/C3vyDyWCnur+WCyQM/jJB5blwHpH7lhuy0p3INc9bEGuXJiS86ebWizO/eMn5w2+zNLLcKw/lFvXBnIIBP1YT+BBvWZGZDjoyi+UJugvSkLcuUgpyQ7+c668roSrB0kt9abmurldF3UsQRYC5YU97N1z0KmOwLNodnmrDu8edPfEqNc7CVEk9eE5g13APG9BN8BFGhMI/dKDD8+JC/MysSgcbXfkvHdObmX1cfeVn7pew+a1zZoR/+Ueqz4fXN9QT/q97bbmo6NgFEJdoZBC0eDdx2rvBJghWrj3VrUmi82GKui7GnVV2HchNQLFqOooMldVgg1vuua0FuY+Q3WDp8QUUK/ejmNqQSkj6FBSoMCz9QtD+fCEsT3O56GqNKdUlCzcs7Ehj3hbOQ/6azXEvSkrA3pZNAzfrt+946lHLoRyp57xYLEhE/ftSf7GvG/bJOa+WWJ3KrijrWRiMHwwTy6yLK00bB5LzBEnb+b+0SJd60bQ/+Zr5XKBlaDXqFb6XZ4sgj8NyU+psP7bDTZJ+Kdjl2JJ44kr9GNt5NK0Cr9dm7MhpNHHpT0jhF9D6UIuI8g28gYXmgw315BxUwpNbd5UPZ0EvVwV99bS9wYvHDzLwdiAOVdr6LybrcMrFCsdbwwr0hmwxaB6KDX0klUPwngZsf9xQydhzJvHPOFT4gEr2udcxD3oNIoQX/z/XJMedA+fnYeyjrkOEgBDoBoB0cUDnvSuzlWQSxaLg6ItVJCTKuRI++FYQbh7Xw7dNRlZcln3+9fTg4679ugCL3ZHFTymKvD3RCDpGmAe9P5AZdpsta5dNZpa1E6ko5szRDaNlhknqiGcOTGapWTTZR5miFeaBn5aqBMpUBpVAK8djS9Y6fAdZJ9j54Q99aTDg/6v/+RdEyn8IY/6OT8i/3isLce7femZXdvXMRhwSNDTCCEzpJ1On/NoAeXnJnCC4SsWGwsE3SWGberD0yjU2DOLFk43rnkussuPKLN5oRzOJPNoNzpbr8ol03V59cc+ORFuOIxj9xc3vEyNTziukl5XCoIn3QwyWL/Mg85UArQl5Z+HRIe1JelCE1ucUia6HgKSlxbtY30IL9dqhDJ40JmywG1sLti7h2hz7RQ5srEhB1dW5bZjx+QTBw7q9iCrly4tyrOvuFw9255uhuK/zy/pQf/DZ/6ozDZaOub/9t775b61dfnv9z8gRzbasmdqWr3ne6an9J2Gjig3fP1HhA6LZjIc3gk6sEEuu8otyENNWYGMY/SQ5+HjOaqM02dDIxJShzytB0Z/zoORkmvg4BFKMDB28DcYyobVF4r576aTQI40anL+zJSmAy1o5AFTDdF6+PWf/duJx93pplz+vTwCmaCXx25H98SEPXDggOzbt0+mplAx/Oy8HqoEHaILQuXPDx2Vb6625evH12X5n/PGn7l/lzxq17RcNDMle1vweFA1OxVBj2ShIkd6Q/nKWl/u6QzlM8f60qiM5NGzWIW68vI/LWdpPXzjT8oCQsgCHY7UXHP1wsV5riHzCEkAqRZUTAFTgj6i4EbuYyQofqfMl9N8ROyjB3AvheWm2yIHhVQrT/t5PBx1rB5OalH1fpohXstJiBfs4goiwwEJ+uGNjpz/R+UJ+u0vfYF6M0OYpLZSqyhhP9LtaZjpN5ZX1JN+f7stx3s9UU8dclRVOUR+6VDD46FUtIdUKPBvkDA8F3jPUVgQZPrcZl3D4fe1EApflUXkkmm7Nir+2gc+1KmOOhvJBZUK/EFBBjFGWL32btZe6QzthHUcMHnoLQwFDAmmaaU3IkGHAeKqt5UrEoc2a7esNUQ96GYACMpeQq6LFvyYhxmTzaNS6FzHlU0nmzYItyVwEocoh66rnTZ+YO1H0RsMbVbRZpEuV8tQEGxjYPUPXAm2EHdcs4e493pt+cxZIOh+U06Gx28ykoCo8Ot8Tgu9jRN0s3Y4SXfDjxN8GLfSV8gft+N4mx0XG8FTbr9D8dLiiZ2unGh3tArvvpmWkcyIZfTvFSNrzoSgT+16DKmqeZZTBbzeaOm8XGqKTNcYvolxv9yvyuqgapjRl0gK4pWXqZQqGVQjG5FSc6UxoEqlrgSjZgQdKSZKyGL4jx4P3iyMJ2+3hJQXzFEor/gNhj3Mw2Pdrhxpd7R151pvYG3xaLTRcNAgn2mMZGgyCfqxA389kdLqa+wfXP+TLBLnBkHLI1cFPOnqoYECTqQcCVX+h1LB2BkMVV5BfjURZttqKplCjQ4asmzEBWyKq2PME7f0B5upIcrBrFJNrYLPvvYgGpDF8KqBXOBPIyPM2AjjKsN2GeKu7xZai7PgnCgS97+WJOgffM6PyD8YQWfjUnN8e9WXkRnAk1t1Mnw6wUUPv2/lFdZ5H55r79VewmapzlEQq6aLjDHvLQm6GwTcLmCyUlOgQNBHI43OumymIf/u4+UJ+gdveJmGuHNckWxHw5hJdzN6BHKtxLyi0Qr4c9LMrZ3hW00Eg85TIDy6rqcGPPsRc9eMkRaSFgqtTZk3HXNfa94k3nSciUYDRiEAsrtXluWWI0flG8ePy998+x7VH86ZnpbLlxblmVdcLq16jXV7KhWNfrn+HeVSF3/vR66T6UZT0Brti/fep6Httx1lDvruqSmZqTdk38y0etm1uK55stVwa0ZS5nXXtftEwwyEOB5Iv3emUGcLdBDItiR+ww3A3uKPEQkjvV8cD4UxMS89lmTXVFPbTVIuirR7KEiHUHV4w9FJoy9r3V6IuUJLyioiiOpV2dOiA2Oxzo7y3RH2Hch/+uKXJpJ1p5tr+fczQyAT9DPDb8f2BnEAMb/lllvkiiuuOGvnOVOCTs/C5pcro1EBPmnAWdg5LBrwBqAwR38g7zpwWL6+siF3LG/Iancgz7totzxm94xcNjstD5sCQYey6N6G4tVwbYj/j0X2/u5Qvrjck3s7Q/nC8Z6g2u/3zVcFhVhe8b5ygvzwjS+Uhea40cQWeM+XM9HLqHUvpBQrHpMAQqnpy2jYtRwkKrGFFl9K5i2BTLUKiwVNgQ4LJu8eLdHSYk6+4IViLkmucvokY9V4jfXmtdCcLSPzoD+w3pXz31WeoN/50y/QkLRUgQHBhrKolWaHLESHXsog51AQPbAXYwTesKPdvjzQ7cmJ/kAe6PQ1VP44wlKxANUbqvTPI9S1WpU9jZoR9qYquHtbMWcVxLtpygI8kvDEaQV2GDmssAsiLrCEoQgVwuV1G1tY6R+gYqo+PxzLtnHFBs+DrW4q8kCnK5e/tVxaBQj6rWus4o7QNG0zE5Qnni3NgXbPjG+iRi2bcKnnLhCeJJ/ZiXt0hZ9c/KRzWGdfINncG76+emUoi/WhLNQs8BMV/HsDOd5FK72qrGkXaw0qNlZMb2tK0JGDXpagv+mHf1y9pZHARl/uuCxz6WHD3pQbV4kSxXWsiBINNDxaUGdDAa3kSSXttAKqI69z7KwNkmsYlL5Dq225d7XNMO1OT/bPz2gOoRo7MOjSdJYgCyK1gGfkV0uGuLd2fW8It3cPuo9teNBRSf2a3VNy/kxddk8hZLIqtxzryDeWu7LaFVntsT85yLuvES7DMYa1kKKyVRoA2WJIfc6MjLEiRrVaMY2A42zENmvBgDJScq6RLpZnDgV2ttlUYg6lFZ50EHX1Bpsndoj1xIqxcQwz5F7DtYd9OX6wHEF/2/UvYrEul+Fm9GOBPMpojhhER/RU6WchKqxxSHsaCiICENpe0WuEobAmsy1W6O7BgDWqSMdWDNTtiE89jnGPevBoFpIyphLoHEVYO4wdlaE0K0OpVOsi1Zq2wVxDAVEj6CSwlNUwOPrzSnPVg6ddc9A78m/OgKB/+Rjr0XA22NquhcN43TSIJTInkT8nk1i+7gy00p5HPsU4MzfQMR4uSsB0buvzTI10yTUEDSR0qqDUSVOAXEaEd6tRgvUNaXyXz9Tl187Eg/7TL9MIP2BD/6p5ZT2aznpsu0FCIyFgeLG1CoYCNXaMGRToGrH10WsXJB50N9akqolub/tp3QiMMxSfRTV3z0VPjAhcUTkH3TD0lcOH5ZMHDsoDGxvyzePL2h7x8qUluWRxQZ526UWatuIm0PVeT15QkqC/8YeeaKR6KHeeWNZCdPetbShxhYEMHnH0MIcBK6z7SSFW3DeMhSDR1LWq1GM6XekOGELuY0dD/i3ikXGPjNjRHH1b392TDnKO8UanQkVWezjeQM6ZmdKQe8g7RAShCjv+YMwFUWdRvZ6PABmB3Dfgjcdcp3F0yQygoPEwIrzty7dkgn5ydedB/yUT9Acd8u2f8Oqrr5a3vOUt8rjHPW77O51myzMh6L5gFa2rXMRiRldUYse3G7+0VLlH31q017r5rvvlq8sbcudKW5X4n7x4tzx294xcMT8j501B2UHPbGcaRbN1VE6s0JCI5p5/9nhX7usM5Usnekq0HrtYk8qgJzeeEUF3D7pbLIiO5+i56hUrlPJ3LFIghly0B6oADgcdCxVDoTYK6YCd3hTcjwjLN6JufaJZ4MxabyBMCV5uEZlpQnGuWd4ePR1YbD3MUkmZkYnU1+YF0qCBoXCceu49eQ0EfQgPelfOe3d5gn73DdfLPHIpQ1grQ+uowHhpJC7QCAnDYuMWeoS7I8ri3k5HDq53lKR/a72jVd/v7/U1J7On3mv2WMbCtcuIOog5POvnTrV0AYTlGIQbIYXYDt8jNHYaHnN46gcwnIBcOkFnWygQAlWg9RlC3QBOLLDneaUkTXwuHroIpRLK5sVvKRe14QQ95qD7kh5nlYd8uvIYnrFrsklYbUHhjIy8QOKpbKYqfzzXeAzL+HZsBwNFYCBTlaGcPzWU81r0SA4HfTnS6cu317rSlrqsyrQMKoiQgHnEjUP0DLKKO4vElSXob1SCXlNB5eMuhJMn9x7uNyiwFgqqP8Q7LCrn0QcScDfF3itau9c59E1PQnPJO5g/OapYg5xqX6QCf2VVidkdx1b1b707kNVuX67YtSD/6mF7KEu8hqIruUYWvBAYDg8l7d9+rFwOOorEMV6EUTTBg65zga3Ofuj8eXn44pTsn6vLnlZVPnVoRf7u/jU5vCFyuE1yDg+4rhM6b2hkJUF3SsN7h5LPkU2CXkdxOCj2JhRDeoQ9Dw/fdiqFOY0wUOSYa8Gt6WnZMzUlUEDh9T3W7so9a+tKMJHzS6nM0YDuC/7CP5VsDvqy/O1yBP3m639KjQN4DTDuEU0Cw98QRIx93N27uNzZEJALhJ3ye7TtHMi50zNy/uwsSTHSWxAdoOHLrNOByKEVy9nvJwXOcM6g+BtW9EQjxJ/rBI0cFWkiUgghs5WBtGQo/WpdBpW6ytUVzc9HeytGbXH+MOIpzUEPodKWHqA1RnpdeeWf/PFECr/rJ/Cgf/k4Q9y9YKA/YxhUnKC7NUyfoz8/u69xfSMYiMxg6uHXDGdwowXHH3qSjxN0FxXRJBbT2Pz8LiViqkyaNhVi2m2Mh7tgykJC0F/90fIe9L/46ZdpG1g1cJkRwyM03Fig88Yr5Jv3XNuwjUbaQSUay+L162gxQy7v1+Sjz0UbI+b4JqxJezWkhGHt1DU2CXVPc89pa+OzQIoFCO4XDh2SP7vjm6oPYP7um5mRR56zRy5amJcfunh/IOjYB6T6hW8v53j590/4AalrN4mhHG23lZjDKIrP/QHD2eEhZ0cJM8q7PmBzAzqZFn/T+VGRFTgTNtqaagMjoeLhxNwigmIRPkawIASdkWY+vpzAQ/dFK+J1zcM/b3ZWdk211GAPubCqtTZ62koP147r7g7wVBlFNKhWpd9s6ERBwWFIpl12LZAdIPTv/erXJ5qvp6Eb+eczRCAT9DMEcCd3/8AHPiBvetOb5Hd/93flkY985Fk51ThB3+5BnYRb4FH0olvuEsKifBuvZK3CKFi63VfgPb9Nxbc8XvQpP94byFuNoN+70VNy9tz9C/LopWn5rtkpJejNKoqA4ahp+dWi5wC/QYFoj0ZyT3sgnzrWkSOdgXx9uSPNqsiVCw31Wr/6z8oV7HrgxheEEPeo31M7cKXcvWiHVtc0b/vwxoYc73Rk3/SMnDMzI0vNhuyZakpVelITEHQQYBYRgTD1nCb15oCkIqy7w1DD5Q7DDtEKBO8nOh2GIGL/ishluxZVcO+abqm3OqqhxbZaqmynLk/TlXWBTIwB/pkh7l258E8mU1hxWB939/3cC4ygR08VwpxJwvkcafV3I0YsOrXcHwj+Dm605c61Dbm33ZNvrm3I6nCktQaQq9ixwktqVYYyYD2WEeYOb91cA/mpLPYGC/JiA6GjFTl3uqk91ZGTBU87qKJ6lXShFEFYHitOW5itVpdnPrsqHuoRsFw69YCx8A69TZw1x3t9ueQt5dqsgaB/da3JEHcL9y1QZ/PMMu8w5pRTWSq2HPKCVG4gc1buNRNcEduubCB/5dW4UgtPHJT+cxt92VMfyEK1KwuVnioMnUFPyRHm5/FBUw70Z6UzrMvKsKnDUVM59CKqmoO+tv7AmRN0q7zrFavdv+gKZyRmdH0pgQnBhNGTlmLC4WAEfYx0qyx0TOw9NaCQGvH/9QggWRUSqIrmw4ykgwrWvaF89cgJ+drhEzJdbyg5u2RxTh51zpJVdGd6hZMG9yimBhMQ9Fd97M8nUrx8vqLNGoxS6lEDeU7SZeDVBpl85sXz8j27p+XhC3W5YLYqn713Vf7h8Lp8a2Ug31rpa6FHVOTfGIqsDZiO0h7C+GB3rxEoTswZPaH/eQ46itBB6Q31C5LoLPcam3WRBalYBwLXhiJyS5CzSvKrqsRqp4gBI7bcG0yCHp8I8FOZNIAHfTKDZBriPlWtq2f84IljstbtyrH1dW1tedH8gpKoC+fntGXk0Y11WekiqxwFQkEGoFgP5ILZOblkYUFb1uGap2p1ekehWFdrAv/Y6pB54dwbocp8+jDupE5QDUtXb6nRbFtCpyG7MBT7HZFhT3rVhpL0jVFF/xBJ4MXfgjfehI+PcV/73JiKc2x0u/Ir7ytL0J8WCDqL3FGm6nkSD3oqcxji7jM0SLcwZQN/t2WvKLH82ByHweFgh0nz3SkffaxsLSX9XIW+1W4K8vmaXCuen4a412uCNrCvOgOC/qGffnnwoHtxN9dTPKTde6Hz2XruOduxafvE8baSW1TKZxTgeFeA2CXEU32wPuL8XhjOCTrWY6ybsQWtqyMcv4fW1uTu1RX5xwcOyycOfFt1HORXP2x2Vr533zlywfyc/M/7z1Ovt8vq5U5HXlQyB/19z/1xzffGmEZFc62IPmAByYMra7LcQeHijnqaW2bYR0QLzu9rgXe7wH5wmKAw7DeOLQt0HET/4eV948fHkLawq1RUL4QTAW0gvdI7xt9Sa0qNEbcfO67RBPiMIpTnzk7JnumWdK0FHaIQUSzODR36fEcVOTEcyIFeV/WDmcpI5mo1uajV0s9thLgPBvJ7X/rKROvEJDpC3nZyBDJBnxyzB22PXbt2yToW9H5fms2mTE9jKY2vo0ePTnwtrjycuPHGpNDZyQ+TGqU1LNDydd3xoVUoR1E5wEKjZMWq98JDErzByWlCXhQq5cpI7u30NXT59+96QG5d3mBxmOFIfuL8OblmsSWXz7TkvBZIFDyhoE9UYhkYxDP4J/wGJRB5ygfbA/n4/8/emwBLdl1Vouvem/P05qp6pZpUJcmSkCVZloyNB3Aj2QzGYIPl6dsGbNpYQcNvMB+abv63HTjaBM3Q8Zvo5gO/2zQzLWi38TzJWJ7RLEsllaSa61W9OV9Od75fa+9zbuYrSW5nlkT0j6hXkfHqvZd58+a5556z195rr7WmmfjTWwOpOO1rFaVC+oGP//VYi5Edu+X33KoboE1TjPSkaS+kng0DhgdW13B8q4vHNts42eni8ukZHJqewd5GDYemmig6pN37IwCdgWSe1lCgF9H7M8PGIJb+orN9X+ia54x/ODcybijIElngb1rciUuadVw63RRRE6GYmT5PG7HlmWpb6pQLbaJvZtCtcJJkjjU4Z3y+4oe49O8mB+gbP6PKxjYDz81QVU5VvInvJQJPIFVMhd20g5V6AjHW4wQnegMc6fZxZhDi0W5fAv82e9JZLTTXgv7GWqHTOpL4lxpRKv6P/ssMEOZLapu2s1KU4Gje+Ktz82KQTyBPAM/WiLJpXxAIIcfXfnZW1pkGIYDX+0IrENyUJWA2tF32oB34fycVidsO0EdF4nKAbWjswwq6gYEjvucWwI/2QRucOISLz3DP2tvXBrbbEgSjAlQA6m6KipviYDnE3lKMQuqjmPhSze3FEUoFF5ViAWejCg77TXSTAlbissnu2+Sb9qB3LxSgv/I1KHpMF2iwNXreVgl6uDTZQH3oEWxBgDwntw5ThC1ExqcB57yXLPnEXAUV/xnpOswDNHlLgiwL0BWcdgYRen6EB5Y38ODKOhabDexrtbCnpRR3PjsWYmoO85XyO3Ir87+kT//K5ycD6PX56yVhyC+uCXlAzoDbg7BQ3nqwhZvmq3jBbAGXNlx8Y7mHB9b6OLwZyYMpyMjxsBVnWA0z+KkjrRrChFKvMGWhOCq8KElK3CimiAAAIABJREFU86lIy+Qv1PZRq+78hXEYM5X1keSv2ZjUktERSmqrVNL5VvAkwO9Jb3UiFmzUoOQ+ozX7kVqepXCnETZOTgbQ/9Otb0Ihc+BHEe49fRpr/T5Ot9tyPZ6/sEOAxk07dwhIX6HVZDBAlMQC4AmjWUXf35rC5dPTIiDWjzPpb50qVyTZUKBdJRz0M+0dHojqtfaTamuVjpWlV4eG4SVg27glcBwbTD7y9UEfYRQgdEuI3AJCx5OHVP7tMXPFfb1P8pSGYW5Y+jzXPFbQ3zsxQH/VCEAf6qrIPDR7/bb1yIidWT2HbWvT+eHNeQB9CNR57JxMb4dvW/Lr/EONMoksCDZNG3rPGCVye1NaR4M8YpGkkKanbAWdAP2XLwCgf/In3y0FBNuDnle0JXmjc0OTx7rn8ppx/jCOE2aJVXnnE0f77c8bd11mhhk7myS0BEfLBJR72tDt+bsqLcaMTekoQNeYwBwTEFG4+9dW8cj6poi2yZx1HOxuNHDjzh3Y3axLrMOKtn0tbdbeOiFA/9a73oZqsWhqFjor3MyTNeL+5RWc7fWlH34j8EWcUuxe6R5TUBG3IfDmmqu2joz57l1Zk9Y9Jsj4ZZ83TBjZe0kZlrtqVfFKn62UhS4v653jYF+zKQJ1dy+vynGrHoF8AYemm9jbqkkMSdYNz5f7nNjlklXIezFJsRQGuKezJWNPavt0oYir63Wp2vfYAhTF+Ldf/cexYuLz74eLPz+7I3ARoD+74/msHu3DH/7wtz3eO97xjrHf70IBuoqo6A4nC7r4LipAl0XSVPAsMLdQU+g6JosqgMn2kTu6SZwchFgLE/zx8XU83A3gJwrMXjQFHKg6Yj0yX3Sws1LFrOmv4ocvECSxZ07e21ZlE6FpbcYZzgQZvtROhGq02vNlK2yV2VcX4Y++MFnQukKATqqQRCjbM+lCcjc9ydy8HlzdwLGtDr6+tIIHVtcl6zlVruJAq4krpluYq7i4pKYqxCrXoeEpg0ZSuigwsjEIhOp1tqvfzw20R5sWQkHKHqdA6EwUwCNgPDjdkMX9moVp7G7UsLNWEWGpiufkHpwax7LH0pqfWlsOBedc4NUTWH8vwIa2RX6EKz86vhVHztx4FwG6VePXjYnZXmUeaDTgOQyVaa+mGw2ZFaycnwlCLPHhhzg1CKRqfi6Mpcd7YKqeDDysQqpQ/Ix4S47KzLURQSQBkpnQbKfEpskRZgM3XvawixVTgXZu+jehuEuwof7pnHvivSwK8mq/JoDd+JtmBPSmwsDxZvVu7x9PJhL3Pvag900F3YjEPWX+nV/N1bR+Pk9tTcmKUlmAOTKN88rQ0ELs6ZcYjTWGYbCtYLAbkOM67fioOyGmnQGaToCqm0qPa6XgoFZUj/PM8XA8KOMfOw30Ug/tWO8Aa7NmKe6dHinug8kp7gLQCTSGNlAWpttEhRmkvAeYoJRVVyaPhtWI7WOR1+gMUB9NRHJkRquL8rkskD9vzbDw0iai7Lusd7bQ7vdwrN3B8XYXu1sz2D8zj4VaWQMywgn2KlvYn+sIjF4XBei/+vmPjRV42ft1fvcLc4/ykDZkDPzMPVDxqIcAvHF/HTfOlXDdlIsDdeB4N8aZfozHtmI8vkU3BgfrETUYMpzop8ICWQ8Ns0NAsTaL6OcYBqvyk95wI/NSRyu32BIaqCM6aQrgLfVUg1oRdSx6As75nSwdEk21NzSSqhaDaQXodgUesnfYkrF+cjIV9/9w65vgJhn6QYivHj+G5W4XJzc3BbA/b24OO+o1/NCBA7h6dhZxwoodg2t9cH9K0kiq7BSoSjMXceZKooLJR8/1UJZ11EHsegrMTQKdWh4q4KbfQ6HAK/AazssMVbYeOAagO0DbH0iVP3AKoCFp5LgjAN0oVhsQpVoB9prZfcsyyLTViwD91z4yqYr7Lbhvk4l6shw0CZ93Wz0DUMy3spEK+NOuXiPoXVtBdDVQ+rf548jNbVk2uQbfyEHPb/WxP1uhLwXtQxBrKeaja4WTMdbJEGSq4n5prYD3XoDNGgE63QNG13UlUwy9z4WfIUlkBegSyxmQblvOhM2zLZ05/OAapWiiTCGkAs9hksLS4TWuGVotsnijlXNxDTDaD/xuXVro6sG45p7lFXx5aUkKEce2ermw2s5aFdcuzEqC60YCdLPf8ux6UYg3T0hx/8hPvFZYSsPZzD1e2UPHNrew4fs40+sKY1EYOUZA1lLWZUwMKFbbvAzLvQEe3Wirm8QIk2p097RJfVFhcR3srhOgUwiPFXNXkoq8jxdqVVnHHt3YwnLfB8sYnDv7pmpYbFQktpWZJKrwTKCqXo4m7YCzYYC7u1simru/WpFixA2tKSngrAWBWNT9yh1fHWufePro4OJvn60RuAjQn62R/P/JcZ4NgC5ZYS44tP6hiIwEARZb6iJt+6PFz5sZetOrUzEgp2iLZK4C9Cd6AZaDBP/5ZBuPdCNkblkqlPsLPcx6IXYUEkx7KS5t1nFJo2ZJkKh4JdQKWpFVajvBd4xewmpNiqXIwdc6jlRMOv0AaRYjdQeI4ggf//pk/rYrP/sGtMqaFNi+ZdlcODce3ewfXN2UCvrHj57EP5w+J1RF0o0OTbVw1ewMLm1WcO18XQRTCAjFA5jUwjiRRZig7sRWV+jsx7f68jmWBwTmGbZIcTdiKvzsWhUGpsqe+Km+aHFe6LDXzM1gf6uB6VJBKN55MoCgm+lW0qEMICdNnteWtk6ygQt4VqDM7wxqr//E18ZexIcA/Y0C0A3NQIZP+httb5ZU6zwBcOy3Zeb3eN/Hkh/gaN/H0YEPtkOsCDB3MBB3eBVMknl3HkC3fZh5z6TyJVVFP0tRSCmPk4G6yLyizVJB6GVMwPB60FddBKcEhGtfmKjAm/9XXU+o8QT1U8ZfmUrSQnWzcs0mKcCAdfcfTkZxf98tWkFn5ZFJAcnCjwA9DYw0wTEMEs3Po5HlCA1fAxH7GlMNM+P4nQF0vec4fpZWq0JTGebRQSsbII26yOK+jCsfi/Ui9rXKCFIXvaSAR/slfHGjLiyIvvLKxfFAIz2luHd7SnH/0uFPjTXv7Jz7zVe+RjUZzLo1GpRrDiOPxoVCzKBG2Bueh24UySPXkji/f9zQ+8+nuj89QB9em20rh2G3MLUhK4dJip3bXMZqZx3rnPN0T5jZgQNzi2KxNldVHQdWN4dspPOtujQoJ0D/V1/4+ERjt2//TYhIoWbCUCrOSsNkcEp7tVrBwRv2lHHDTAHXtjLsr2XoJy781BOgfoIOGgPgRC/DqUGKR7ZStKMMZ31NyKlPsxWeGk1qmEqmJHKGvB97vWzuyYrEUe196JM+VPjWnnRIwEvBNQLboldUgM5+zSSVdUYAurRODb/kGhKgn5gMoP/7W9+INErR9QN84bEjOLvVwYmNddB+7OD0DOZrNbzj6qvwol270KD+hWfU6AlQEtLwWfNWpgFZVZ5blKQlE0YE6VSIF02MQlHGiCJxXLOp1cE9VSzS+DMrarLOqtkdF3KO31SB+zLQ8JQdtNIP0A5D9DMPTGWT+RCzd1UYQboP2N71pwJ0HTdJSpnrxUTE+/5+MpbaJ19/M+43AD0xIooWoFsLtO37r7a1acVaP+8zfdmEmD3f0Xt1VC9C10fDTjFK2U895nDtsOupvkZZa2qFqK+SRLdRvh8yadQKjeeuFXQF6L/02fFYGzy+Xe8I0MucE7LR2U9rkxBayFBbNRVd5RwhQNdx00TI9gaf0ShH90+eM/dBsUrMW72G991of78VqrOWpTmwJ2uG/dB5DkMUeNCNI3TiCHeeOYNPnTiBQZKhF2ciiEmAulAp43mzLeyq1/DCnaygD+/bgCJx//W/TLTW/c7N36fODzaZKgBc1yeuwYMoxtl+T/rc2a8tbYWGLWcT1mrfqEwcricbQYCTnX5OzxcGm5kyNheUs9ky3tcudjdo6VYAExE8Fq1nWYAh5Z1705mur8nFmPujg8VGGQv1koixFinyaFInlqXGohnXBqmgd7fEivaaqRYWyxW8dHZWElSnu12h8P/sp8db677NbXbxT8/CCFwE6M/CID5Xhzhx4sS3PfS+ffvGfusL60HXDaYfG5pglAhwlEyiydZRrIKLEMWAxOPYLMCkG3JBrwt9GagZRc+Cq1ncxzoBVoIEf3G2j8cGiQB0wq1atIJS0kPLGaCGEHukIlxVn2m4mCpVMVdmNUnqSQLQSRNkr+NyBKwlBXwrrEj/HumirEoEaRtRHOKrd4+3CdqxW3v364cVdHMFhkl5W0HX35zt+dLz+NUzK7hvZR0nu32c6gwkYCRQna+UsKdeUcsk9skaARsGV5s+A8hEethD9l8HoVbWRQAkE5sdrTzre5VdpYhOlUjndHHFTBM76mVcNTuFPQ0mNirYWS1LsoQPiqCx11wCB2ZprZ2OJF908yFQV8EYpZ9thTFu+tQ3xtoAR4OH9Z++VTxarTWJBEnmE9gM+lZCShZ9wwOsh5FUy8/5AZbDSIThqCy8Rb9PUzmXcJMK7gKcVeHcdIXJ5qqED1MjSTUgIOBmoqJmqkgMZwiP1EPZkY1a1VENndZUWawirQJ1VjwKAuL5XIJ0jiuBADdYUtVUKZ6BS4Ze4GPPhAD9/bfQZk0BurVZk8qiDSLPayC3gH0U0GyjK45WcfPK+xBAbgfoNggdKT3ZeX/euNSyPkpZiGLUhpf00fIS1L0EM+UiZiocZZVR7KYlbKRVLEcVHAlbEiBKj14O0DXEI8W9IyJxA3zp4ckA+ocI0F3W9o0tlJkP+acZofTzHuS9SAoj6YtU3qUNHO9Dslme7kvzCUPBKMszz1WQzYueSqfXP1hpRDIKGPp2g0CqrO3OWXT6a4joaZtmmGvtxI7p3TJHmezY3hk/7IUfPUcL0H9tQoB+xaHvRpg4qn3hh7Ie2Ipi3aO9GvBji0VcO+Xh6nqMvZVUwHmYujjRjQSgnx5kON7LhCGxHBZkDi+H2oZEUGK0oTXVYwQ0bauQhUg6UMP5nn9GM1GtmJz6Dw8vKMGR7DtFDw1SUXl9vYIA9DaTfNT2oO0aA2p3dNXQdyBAXz0xnmCX3Sf+w61vRpG2kGGE+06fxmq3h8Mry9gKfBG6IvX+xw4dxLUL82gW1B5OIQqrmwzKlYLPBKamHtXXnb2itF5idZ302inasJo9V6jSIurGfTqRvbWdpJJAD1IFX4VMq5rzZVeSugTnFMM83fOxHkTopew9d5FQA4ItRlapO99vrDDpUChsVDBMr1Umc/hDn7x9rL0iB5nPCNC13350JbI0a7W8VIBue8if7n61VV67L2iiy6b47T05vJ/tvb39WNuBuf2bJjwZm6TwnEwZVp6OlySS5fpZnQVdNwjn+HfuZ0ysH6x5+MULAOjsQWf7kIyBOU2bWLF7rTiLGOHEHJybBIwq4eh+ORxpc98bcM45yqKCsNVMJT33PTcsFrs35VR3qyvB8zJaI1z7LM2dLBHWgE/1ujjd7+H+1TV849w5ae/g2HBvrxY97KpXcf3CjDBLLp+ZlvXavhfn3KQA/d03vCAH6DbRzT2dMYC0+InIJNsLKV4by7osLQJSvNC/5zZr0mJTEMcIFmi4zvC+HE3R2jy0JohVlJef8ZJGFTVaydXKAsi3AgXoU9Rg8lyc6jCmjNAN2CvPyjr3VsYgnG+q5cN4jTFIi6/h2lIu40zo45vtDdG8uH5mGjvLZdw0Nb0NoN/2mYsAfWxQ9Ry+4CJAfw4H90IPrdWBp98IeGwqwo77dSEq7nYDXPUjrAYR2gEFdyIJLKZLzOgXMFMtysZOAg7BOYMDIiQShxgotVxPxLRmCkbJkxX0NMMjWwToKT6yHuNYkCFzK1JFbW8ew6C/hkKyJQH/jloFsxVmC9n/62Kx2sTeesuAc/WmpBBVN3VxLvTQRRmn3WmpAvlRgjgJ0Q1WQF/lR781XiXYjt36z/zoUwC62XOkD9yGnHptVHX+yGYXJ7Z6+MrSMr66tCwZ2XN9XyohJZcVV4JB9eUVcZJUAyyt+mkQz8qk3Yj00JoMIdDkNOGWzM2QAJ1V4Nmqh3rRxWXTTSzWqtK3emiqIb3WBJaMCqgMz82Fm42yIkix1OqL0COFXjUMyDphgpd9dnyvTDt2K1RxN+0Bw15pDY4C9lJmwHHT7vBwp49TfR9n/EB630ndlODT0DX5f2bXpXLGiqdLKqtpdzCNl+zL0oq5gQHG23y6XBQwTeAoVTZDw+P/OYay6TuOBMMcm26sFiYSiNKWyPTLNotFzJRLpgeN1W36lXrYUanihoU5Ae3sY2fgveX3se8PJ7NZe/8t7xAfdFtBV+/T8wTK7FJhquuj4Pypqu0mEDWvGQJMe8xhyDm6xpxPeWRwxy/23xeRohavS0Kt09+A73exv1nGYr2EHdUSFqolnOj08Fi7g/W0jqV0ColXQ1ycNS2a9lpZaj4rBOEFA/R/+8ofVoBufZqtz+6I4JlUTbhGmUTPfLUqdj6kWkqVNQrFD9emw2xlzYaw9meJiW1QfP7xLTLYNqDKttH359x1cbazhc1BD8HgDMJgReiqVAOv1Xai0dhj7NW0SjPcHmzX9pBaqtVXpbj/6zsmq6C/4KqXoh/pusAqvtDcDfuJyS2KJ/7ADhff1XRweSXC7hIrS6RjOwLQT3ZDnO5nONZLkXlVpF4TvRRYizIM0hQbXOPE4olrGY3U1TKJCQFZR0eE2/TzGiBl5p1lbhCgC/1bvitA50sl+JdEmgJ07qlkZhEkkYEkic5EWQicI9Ira33FjQ/6yvHJAPofvPEtaBRKIt55emMDbd/HXQTq/Z4ycjwXr7jkEukxn6IdXEHZO1xDuI8QQklgLxodDuKULRcqDsq9dke1ima5hL3NpnxmaRsxmhcEDhTB4xpJhwsKMvYi9f+uiWWTg91VbeFR0n+GJ7o+zvlMgAIDsiak8qYU36G4mO0T1u/ye6nAajqKn1XvgQxBHOHff/a/P4sAXVefpwB003/OfevbAfTRHKbtdFZmzFDoTE9+mBbSdXOYGBqFrDn6HbmfbfuH2EsiRb3goFXg/aD93dzDOqTRyfuYSqsBxkE6pLj/4gVQ3D/+jn9u2h/0bUTqwSr5mx5vxmaW1q5jNkx8DLsXrHaNaT0xIo0ijErLP95LdAGwOi85hVvFai313cYs1vNcPr0MpDb1UG9GklKS1nBx/9oa7l9bx6luF4+326pBI77iCtD3Nut4ye6dkghnNZ1z31rI+lGIW/9ksgr6S/buF2DM87UJninDqJN4wfNE64drIUE6YyNReJd4TSvqjG1ofVZ0C6h4RVFwf3xzSxMzuUOFuS5m3thtm3cvE/9sS2RMzTZFul/QWpPHnqtVRK39xNYA636I1X6MrSDBdIUJWw9lJttY8JL4LZX9a0e9Kgm8xVYTp/wBvrq5jtlSCTfOzmKhXML1jZb0rtsK+s9NkBgaF4NcfP53PgIXAfp3Plb/5M+87777tr1nFEW455578Du/8zv44Ac/iNe//vVjn9MoQG+WWaV+5i+bpbYLCBckLhQnez5O9Xx0owTtKMZMqYBdVMEuFjBbLYE6kVtwJOu5Jfg8Ewsb8ZOWYN7BjpL6Stc9zV4/2g6xEqb4ZDvFiQAiUsOgZH3zCfT7a3CiDpykLz3cPG9SlLldz5er2Fkh5V1XfYYJBJyDrIDNtIKeU8GKN4uIYj0xK8UBBv4y4tjHE5MC9J/+Yal+2Oy0vLPZnPjetrtSR1ZrIsusBvsRHl7bxOH1tizcS70BggRC39LAi5lSBcdWLEWrwqpA3jJJDZuRZjBLkMZxF8XiEjcF0rPZP03PTg/VgosdtTK40SzWKpivluRYfIzS2mNTMddssAY6VlxIP5oGR50oxgs/MXkFffWnbs170G0ZhH2j/KLvLqn7D3X60mf+ra0eTvQDSQaJoJNV6ZWeU1cSGAyySW+lCBhBOgF6bmeVK6yzisGEjnqIS0WtwF5OV6rfHAsNJrSKzrnEjZdjQN9kbsb83L2YAk7ckBMFc8hkw5Rkh0xAZrAJ0MmMqOC6uVmhkx1oNKT/H0mMQ380mc3aEKDTC9n0wpsJ+HSicKNq7tuq6CYAlW+jdPjRn61a9jMuDedX0jMUkwHcNEI52oAX9+BlAbw0xGzFk3moiQ0IS+asn8D3WugX5oUpkxbq21gOjhQaFLI+GwD9g9/3QyPJLwUSli6b95yOCPiwEsTghsEWmUEEbgToZLAQiHANtHPX9sbamNMOmVblbCCm/7HXwb7WguuhCzqrlQ6ObWxguduBG63CSTYxU6lgulJFpTqPWn1RAlJbJc7bGc6fC+ZEbAX939zxiYmA0vVXfQ8GAtBTrBmAbsgoUnllsvWVsxmeVwcuq8RYLDKxp0oETOSu+THO+QlO9RN4xSpKpYZUZdnn3KFGiJ8IW4Y96Ww2Ye+zAHQjssnEmibYhuM3KsJF0StaWvM6iao8v5u+dSHWCOBWxgx7L0mpJ0BnEEsqOK8l1xHxGZZK3LB9RPpiswTnjn1horH7f259q9DQpfJGIc8owpHVFQHqdCrg8dl/vlirS/KAa5ToXQhFliBHk6ZqlwRJJJBmS19lfk4CFIKBg1NTUrlrlNgW5sAVVWuCbFWOXo0TAQftUI9HFhtZPaygVz2yI2Jhnh3tkakUS7sJk6XS0Wo8uoX3kgPy7VR3S92WvVcSK1pBJ0D//c9NBtA/cX4F3fif89AK0LmrKjCz9xOTGfy8ytExOgL5fWB3MP1FHi8YwGpp3dvuU0N1VoA+rLBbxszTLY+2ZYjJ9BJSNAqQfZuJ55BFAgJ02hWalBzPg3o8PGcqaauKOyvokwuxSgW9pGJntj3E9r5bZxGKwgndXr6raJzp/hphJ2yvolvtElVfVxtTobjbBIYw6ocJR+M2mutLWKq76WQySXPuucoi8FOOEXD3yiruXV0TpfKz/b6MDc+NlG1ayO5vNfHyPYsCYlvSsme0ZhwH/ZA96JMB9FsOXiY96PycvOc4t/Y366Y9SxP5TNKzR552b4zXCNjJrOI6wriA7EDq/ZQ8Beg8/8Nrm8JkYYy3nfkxuqbp/BSAXq8KS4oCl4xVuFbxdXTloSjcqS4dgSLRJNoMYkyVtXWR+i6MM6TfPU0F4C82apirVbFvagrHBn18YfUcGl4Bl9OirVDEFdWq7M3Lg4Hsce+/c/zYbmwQcvEF3/EIXATo3/FQ/a/zxI997GP4rd/6Ldxxxx1jn9QoQG+Uy9sWjKc7GBcGK+DDxYmCOoc3uzjc7kl/cD/JsFgt4bJmVWih89Uy+nCwnDjSx9Y2ghVSweSOESdSbdtb9YReN1vQjfTRdoy1MMUXew5ORw56IC0vxcr6E+KDnARdpLGPcqGMUqEkgXuchmiZ3l9W06X6ZQKsxC1j4E0hdKtoFxcQgWIbpCwGiPyzSKIBTj309YkCr7W3v0oCo9EvSzW33/N6llQ1rJSKK5lPPqjEfrrbx9lBiKNbvgD2o+220GnZ40RaFYEes7YzZXp0u7ikQiDOjdEEGNwgXVfEQwjSD7WaAggJOglEWWVh0OlJXxJ3ayX/kc5OpWArAicbtdDuNPDRSpUGP0wOCA3PoAl61V/x3+8ca9x43Jx98NNvks1HxeMV6GiPHoVKWL2J8dXVTTzR6+P+dh/HewG2ogS9mP1VrHSzD62ASrGIrSjEqh8INbVULApA53dLdecYihq752He+IWyYq7tFuo3zz5zUURXviOyVMeFQjDcfM8N2O8VSr8qq+gMsvl7FbcZCmaJ7jJ7yDyyFoqYK5dx9cw0dtVq+J7FRanUzxU8fNcfT+bRSoD+kO1B95Q1kVd2LF3dIj7bi573kw+ljCydzlL4hlWl0cr5UCX7mRYYG2gIa4PhsL8KxH1k/iacuI/d9RLmykxhqDb5E+0+ntjqy/0YuA2Uq7Oot3ar1JCp/Ok1MEjgWQToH/jeH1QrHkPR5WdSyqeuPfwDK3/KjNB+aN5PrKawajFbrYrvbIfU8ySR/9vPrxY2Wk20VaqhFZXp+xyWpLYNpxWNIxuDl5MgLEqAby0v40S7jZbTRd0ZYFezgV2NOkqVaZSr85ZQn1f6ZCZso38PK+u2gv7rX5wMoF975UswiHhuKTYG2nuphSAmXdU/+0XNGJdWEhwsp9hVZGDLu7SAyFSV2Kay7EeoVSqYqtdRIiOgWsNaADyylWAtSPHoViTAcDNRwbPIeAkjTVR0UxYMznvLKlPIFDlsbMrEL12EG3lveEZUjvZssh8QRLhCxVWavKZEuCraijSnG3tccwtCCfrZBpRgaUKA/oe3vhXlou6xQoGlHWS/C/bJBmEoDDhS27kGVYRJxcShAnXOCT7MoqxJwkD3huVezySBU9kbrpyZlv1oX3NKE4blktxT0ncOYC1m2wETLGyRSjHFSj0F9Dxde+ku4acJjvYiLNPC06jCC9QSirveL8pA0WqzgjnjVGBFRI01nc76VD7nH9zxkbH2CrtPWIC+EZLRpbosSqaw7ggEM7p2s+bJfZ+tX0xiSB+1AejKZ7P1cgvO9X6To414edubU4DoeWuq7TE+v2gxXFnzV8t9WXUowpdKf3+roMCT85sgtCNTWqURBaAbBwc/SxSg19mDPvke+6mffDeqJTPvRj6/hF/W+tMkMYSibVxvrPXa0DLTjtyQucfxFg0UA9ClF90mPCxLTUZfGZM5qcvGFXbtzfv7eWwdh60wQTdO8Y1zK/jmuRWJhSiUKy4CqbZ1NColXDo9jX+2f69hviXq1mKE4gjQ/7c/nWyPvfWqa0TFnV9c5xhP3rRzXlibbD1gPGGFe1ndXx34wqrqRJFYKXK9U1FHBdJ0G7Iq7rzvmDwaMjA0YT3K2LEVdLbGsUWRSTe+J48lxYSK2qydE62ICKe2+lgdBMLm5H7FuIoPSSKLqFwF+5p17KjXccXsDI6Iey9cAAAgAElEQVR0O/j7pdMSt8w4njBpdtHdhIWIJ50+GNf85/seHOt+HRuAXHzBWCNwEaCPNVz/azz5sccew3XXXYeebNTjfdkNcOO220CA/nRf59fHFApAfSDDGI+0e3h0qyc0RganC5Ui9tcrktXnotLLHJxNXAQC0FXAQ1TduYCzf851sL/KioZW0nlsqv2uhxm+3HexFAOdLEGQJVjdOIbeYA1p6CONQ1QKXKTK8OMe/LgrvezshaQ3Oj3S+SX1B6+KpDiDyKuhV5oX+hQz2ATosb+EVAD6eNnC3GbtrTeDlKfRLwvMmXndDnK1aiPBTuZIJZaK8vTHZAWdoHSpH6DD/urBIKfoc0Fmnyk3CfZDs2q6UNL+aAr7SCDJhIRLKntFxn1XtSJVojyz7RZMhUFqG0q/52ZnqsAMtHneo1U4pcubY48AdI1XHFFT3/c341WU+MpRgM7Mdy66ZarcPH43YaUnwwObHZweBPjmWhuPdfo4wyq6H0obAK8t++uYSacWAivoIpTk6ViVi/zOHnJtgWBmmX3Fs5LVdtA0lijGuEfGU0VrdNaT6cG/MRD241h6yCSAZXWfokvGb94gMg1WpcSnDgYq3qT6Arvpd18u43kzM5I0mfVc/MBf/c1YG6Adt1GAvq2Cvk0UzgieWfD+FBuwEUq0bZcY7UXPgZ6ZETa6Gpnoto6kOZUUTuKLHZTnr8NNqBMxQAlse1Evao4jbdWW/RTLQQqn1IJTmkKx1EKlNjdSyzIslFwvQCvoSRrCqrhP2oP+/lf8gIjEjQbZVp/YVvbpnxvEsbQnEDDNVcpy7QjQSXfndZcgK0kkkOH81cqOCi2N0n1zL2hD/7Vz3QJ4+12twrQ6x3Fd7keyNjy4vILjm1toeT7qboj9U015lMstVCvTQw7uEKoPxfrzSroBIKzMPZlQ+L/+4ZMTzburL3+RCkaSPmyYPWyL4eclIGLw/NLpFJdVU1xRdbC7BBQLVXheRfQ+2EfdDgPp3fQKBRQKJQEPU7WGtGuc7GdY9VN8azOUn5dCtkapdZjUGQ1TiuCaeFU1MQg0VPBMAIeTyZrAChsf0r1jLevoFpIpY4ggXbMaqnJMICfU0zihhIWsD1KQtcJ1pNunMc48MSHF/SfeigIr6AbcCrMrDCQ5SutMaoCwss91iykNgh/eM6QMW4CuWiuOCPRt+oEkiQjQWdmnVSjXmt21qthqHZhqoVYoYq5WQ5HBOq22XApp6j7NPYbXkUlLrntaZ89wLghkXzrlM0nONjGlOzP1Zhki4qghGg7DeS/3gGHGWBs2FY/TRxhH+ON/+B8TzbtRkbg41dXZVl4tbOT6LtVT0wNNJgSLBnJfmr5uWZttJ7VZzwQaG1s4ge55K8ow8hGf+aese8OObEnOjnQU588162ndVYcQgvS6wwQJ0CM7TrzlrXWoMja0B52aMgToLg5RJO5zkwP0z/zUz8q155dNUIyuVzo+yjIYqrgbm9ORpIWmNeyYGHcEq1xutFWsWJwUX8xzc52WUY2UYTt8DlKH4F1HcqnnC9OQ9PYH1zZkrQ2MuwHFQqulEmZqNeyfmsLL9xGgk8WmAN3aqBKgv+PPJgPob73m+RL7yLWI1bL2xYsLoiXDajbjKq4XvAeOtqmkToA8ECE4YbpkKaZ4jpWyxAEE1kvdPh5a25SKNpNklm1ip5YtJugkTSV2oesOiwp0ndC+ewJ0T6ryjCXp3kPdjLM9VtJDETYl64bJOQJ4bVlIsaNWFZFgqt1fOTeHx7sdfGLptKzjhSgVK+Q5aWHJ0E2VCXD74UfGul/HQx8Xnz3uCFwE6OOO2D/h8xmcj37xRlpaWsL73vc+HD58GPfee+/YZ5MLnd12m1DFz/8aLsfn704ZVvu+VFGOdPp4fGuAYoGqkgVMFT0slAmAte+tkzk4E9OqxcWWoyCRYFx6ezMGJRCA3vAc7Kkq9fh4J8FmBNw18LAcO9hIQ/hZjLWtE+gPNpBFlKKlCEdVquhtfw1bwSoKiFFAKH3cZQrLiWc34Bbr8Co7kBVqSMrzSB0q0nrIkgDJ4DSS2MeZh7451mJkx+70rd8rFdE8C58HYEotEmVlI6DC8WEARro+H0I/Slgt1qr5IGHWmIQzm011UKbll1ES5+9j+pvTr9ZTRVhWx4XKWKCyryMVExGZM1Vdn0FWwvchwGHvolIYtwN0/qwbI4VHWJUWgCvVGwJefQ/Se/WD6qclIJ7+0/HEus4H6KwSWNE2AcXGgg8EUVQb9VmxjnHH0goeXG/j/vU2jrS70g/JXjRmuetldjyrqAyTH6RiMuhkppufh1llbm4NwyLgd25+JUeFoJjtlnYEE1gIzZ2VUxGFcdGPItUyENZILEwFEcRx2CPqKNWdFlyZVm04XlTOJdVW++AN8DfUWV6zqSdtu/7VZz870ZwjQH/Y9KBzbog2Vg6uFVDr70w1NQfpwocY6Vc2fbwGoG+//y1TYlhBHwlZ5akS/oqmRCrgKfbXkMYDlMI2vDTAvlYR02UPa31feudoj3O6O0BWaiAtNNCoz6HV3AmXySNPfc9zgSbbVmErzhSpGlVxn1Ak7v98+avEitEmzkbns1gKZhmOtzex7vsSjFFTgDaIlzTqWKhWMW8SYARDSjc2AopGjEt7hJWKzM9CzQLRb8h/1r+LIwKBC6mOJsCX+9shgMpw/+qW0BfvO7eG4+0OmgVSXlN819wMrp6bRq1URa1UG+k7t3fldvX2UeDA47MH/f1fGu+etWvdwYM3wE1TqZYvlClE5EhRmwHpsT5BdYKbF1xc2XRwTaOIfdUCapUZVMpNpMkAadLHIOyjF3ZE0IhtUY1SBTsaTVEV78auJG/uWgu1ot4lkKG4pyY/HI9iW8CUqwJq6z2qm1OZ3BNKPMeOFPfpSkkC2XqZIJ2CaEpFTVMXSaLMG6pN29Yh6Xs3zAe+D9fVakGTbOIsL9QS7hcxTjw+nnBSLhL3urcA7EHPK8sq9aa0Zq0yMtsg52SCd0vpJ0BnOkHsHV0PW74vXukUEFzpdmRtGoS+6dNPQS2MQ1PTaJXLODA9LVTbvTPTyjYqlSS5KftSBtMio84mnKPcz5f9ECsx+6OtFRdPTanXAs4NvZ2ATirp5rvYb9rPN3Iv86OFcYg//8pk9n6ffv3NeKA9QDtUTQOOllLbNXEj+4ZpTRIr14w+8VyLCU0UoFsKd765mjXTCpMJJrIA9CmlcbOYyotHQbyumDaRO+yztuwMXX9bHtCktkkWy4PCpptcFxiHuAXZx8jqE5cb7l+kQGexAehFvPdzk1PcP/fTt6Fu2JG2Kq7fda0btgBQzd2ouJu1zEJyK7ynY7d9F2C8ZoG5tHqZhJYF6Ragi57LSP9+HkWYfWso5KeCEQ+sbeDIRhvHOj0c7/SMDSxjmQhBFEgFedfUNC5ptXDjnj3izlCUK232PrZ1PFlZftefTwbQ33X9tVINZ2KA9HXu9y/bvQuL9bqA5jr3d9NWeHh9Ayc7HZzudLHcH+QWfa0yKe5ljaEcTxL8tMkkU4ctcnYf4ElLYsklLV373skSYCxE4WDGYU0mBTzGMOxpZ0ufJgeYhIqyVApmZNYIu4QtpJLsUzFUromL9ap4pNM3/vnz8zjW6+KzZ5fEVWKdtnVpiprpwwrIokhT3Hni2FjxydgA5OILxhqBiwB9rOH6p33y04nEMbDbu3cv/vIv/xIveclLxj6hUYCuFfRhBTUH5+dtVgxiuYGc6w2EUnOi6+NEzxd1yEqBwJjiJtwgU/Gs7qSsoBOgawWdixBBGYMggsyK62BfxUWj4GBvVXuCz/RSdCLg7oErAH0lDtFPY2x0ljAI2nATwEkJwgi0Cmj77OVbQdGJUESEokM6eJlJSBFVI0AvVnfCKdTgVgjQXfF1TZMAcf+MgIrTD48ndmbH7tiPv0z8sm1PFldXG6gwQOD/JQMvdDB+bi6aqtTKJAZBOquy9C8ncGaQpHYs+iBA54JcLxUkqKK3J1dg9nvyeaQzqTCagsZqqaj0TiOG1g9jRDGBhCvCQlmmYNS4yWrvuVFjZSKBWVcB5uKxy/O2ns1D/2GbH6fmwI6/GA9kPh1AH/bpczPRCkkgQAVSPd+MYnzl3BoOb3akpeJop68+rRxTobKr9iurXVJZd6z6uoosEaBzLMks4JiRMsZggUCcX5bSbEE6QS/nIQNdyZQnFJDSqjk3LttbWBXqmiYYbKDKv0vFqqh2f8KhMG0DFhDwmFNPIo33fX489sFoBV0AesIA2wD0nCZokbkV87ECcgrcJEiyfvA5iLeVJR0PjV0N2XCkKq91ljy0Ep9poTfHAzhM/PhrQBJgdylGw01RKnAupzja7uFcz8damGEzzFCsTKFYmUal0kKtxiqwB4eg2VSZJQy2dFPbvC0APURPfND9iVXc//XLbpH5kQedMr+1T4TuCLzOx9ptbPgD7Gs0xH/2iplpAemspM9VWRHR+8T2BKsi/BCcqPaFPgjQ+RdrUSisGvEcVuAurxsB6BTj4rw/vN6R1pcHVzbFWtGVOnEiQRYf5UIJlaImVXkVRitQ2wWwRhTlDUD/wJc+PVbgZefdpQevRx0ZKk6GK5tlWb8HgQrnPdwJsBEluGVnAVc1XVzTKmN/rYBieQrFYhNZOgDSAfxoAD/soRelaAcJKoUSmuWaCHd2IgdrQYIHN0N0EwdnwwL8zMFarN69BDbsa51mZRwZ+iHppClWQgeDhD3rKnE2V61Ia0m9TIaNgzDl/UuBRybThkkquW7GlYJUbiuAySRAmciDgN+KfHC/SGOcOjIZQP+tH30TvAJ70Ie1SGs/lVcc5XZia40p45pWG/WN5pqvLhC9IER7MBBv8Q4FBGlFFQxknWLVkMmjA82WBPOXtJrS+75I1gVpwSWt6JG5xCQmtRVI52fAT82Ph9s9SYquRik6ZCiYCTYE5gagG9VvXfes/eZQHE7WvrwcnYmV6d98fbLWis/9+M14QGzWqP2h1HqVsuP1VlgtvdC8bhQQezL5ybVakmcC4rX1xF5rSSgYxoskMUea+0ahuBXlPV+c14rk2ZVQkgKjXuBGtNPukzUnQ43rTBrJY8DiBQeUAK8g6U25u7V9jFeba0KKRtHF5fUS/sUF9KB/5qfeI6rdPBcLyOV6mZ91j7JjpO0K/DvHKE9ZmLVYPm9ODzCd8xagy7kr0NQxtKwCTS5xz9MxGq5VebJkRAdERdYy3LO8BgJfiueeG3DtIMUvkUSPH/mYqzdw6dw8djab+K6du1Qs01GhZHuKgydbQG77y8mEWN99w3Via8t5RIDOYzI5SrV4Jm2Z7LIFjDPdrrjrSAXd983aD2nzZBXdFmKUGRnkWheM90RjyOx3jN94TJlvmcaBMxUF5BK7mAQd473M2A2KcC1jyCd9y6lJYdcwxpzSmsGCB4tfjTqeR7X2ek1YfKcHfXxtZQXtgY/TG22x2C3ZvckIc37j9Imx9omxAcjFF4w1AhcB+ljD9U/75C9+8Yvb3pCAfWFhAZdddhkKRq163DOygdeqVNCtf/jwKLLEGoBuF2YBKUmKU52+2IaRlr0exALOSTUmDYnerrS+WvJjsWlZTTyxAdsgWCZdu1wUYMN+ddK49pTZH6wVdGZCNwOgnzi4r+dgJQJO+JHQqTv+OoKoh7JTRJECQimpgTHag7NoD86h5MYoOzEK/DsFp8SzmwC9gVJtFwqFKurVWQFyDObS1EfQO4MkCnD6kXvGWozs2D38I98tXsTcliygtcG5WNtwYzG9kqQlsSIr4+owcNReJAJEAXasiHOjYc+46Tli1pQLcrHoSfX2XK8jFdsw4gLuYqqqtmx8jvZOKmBT7JShGyhdifa+DGykGiPK5KqcL9V3qeySOqXnR/Chm5xRtZUNXamt/GfBCPtJD/3t5PS79Xe+SQSRBOSZnZUtEaS1UsyIQnHHmQgKQjy42cGJ7gCn+xQvCnOrEqWvpkptl+CM/cLc1CgGp9dEkhijnqNS+R0WA/hZhW5melyt0N58uST0eKv+bMEpN1aOBStUzRKVY5WaKqG/JAI0EB7E2qoQUvU9CCRg7IaBgP/pJ0VcPvSlL08056SC3lcVd6W4KzVaz08VD5QCyr54BST2Oosnu9zMprYq3zKpFlLuzlYgpCrCQMAcSypNGeuU2u3O4/C9JUTurwCxLz3nxSzEK/fMYl+jgofWN7HU7+PLS2t4eKOLapXV1ClMNRcw3dihzgNuMRciNLxzO/NM54CKKFmbtb4AdNqsTQYy/4+XfH8enCoFl59Dk1ydgBXZGMcNQL9yZkYCmxt2LODq2RlJxLG/V3uI9bxs/kDmMFkZooSto6v3jBWiG/agK3gxQO28nnRN3mVY6lH0J8Ij6x2ppK/6pDAGAj6p7cHz5XW0SR+7ag+FzfIrbLx2dXawgv4bd042dnsPPh97S8BcwcFrdtSws+TiXKcv/dBfWQ9w9snvr1qs4qqpIq6dKeNAo4TYqyPxqiL55mYh4jhAFA/QCTJsDHgfMXHoYRCl2PCZmE1wahACXgG1qorIbcQE58DjHVVJni1nkgiuemzTyfDQJvegDFsxBd9c7Kw3hObdKjuoFlXcjY9BTCBP0TkCfk2SkGpK0S7Sjll9IoVaROK4ZjPxWeR3IHOJDyKceWQygP7rP/gGFIuqVWKdmhXoqTiX3Ktmr3Vlbcp510rtpyaCiFECcRwjelLEyUkTOEmEfhRgrd8BWzNObm1KInixWpN1qFWuoEQrvmpVvs+WVXTq0imOURGXNOsC6PupCyqH37PexUm2EUUJtlQcRM5Zk1FGRMxS3AXoqnq1AD6TvLdUd7HHM58pSiJ84q7PTLTe3fkTN+P+jT7Wghgrg1BYSlyJ2CYmVXyxylSPe96jwsoye5cmFpSVIr3fMV04FNRohV0/g105ee2lX9rsi/J7YXXZHVF7koXRYZKX6umtr1OmmdLtOVe5V7A66WYJ4pjzn+KS6j1O1qH2h6tKPt9T9hKyDF1H2IhXNir4yc+MZwHLc7bxycd/8mdRLZaNYK6OVSSfeCgEZ9sFrPCqZXnolmzXLV3UrAbRcL3RcRj1Nzdbiuwrdu/k/TS0PNWsohxZrBSHQoPs42Zv9zeWzuH+1VWJXcTSki5FSQQ/DtALB9g7PYMbLtmLqVodizNz6sMuiqLD5G4/DPAr/+3PJ5pzv3DjC9CJuB+ozgivNxPyjCekJYTXiIw8EZzldXPUas3EcozRGBMzUSg6Q92+ofOr7RnjPT7Y8mWT/xw3Hs86iPCYbI/jd+4jyrjiWgbMlKtSbVeAnkj/O5l+JuxTlgnjQSbiigUcnGrhuh0LolNxSaMhe8nj7S2s9vt48OyK9M0nsaa7eK/wnL61fGassRsXf1x8/ngjcBGgjzdeF/RsK77x7azTLugNvoMXjwL0BoXOhuylkW6j7QfiYsWenNNdH8v9QGiKfHDzZ4aeNBsKdrWjFGeCVCmKDIyyDJsJg1gXzUpZNtEDjSoaBOglVyxI9lR08evGkEDqvi6wEmU40ouwTnGwoIOA4nCskLKiFveRxAE6g2V0/GWUnBQlh/1CBJr0WSYIJwZoolhbRLlYQas6I2DEZ6CTBPAFoPs4/ej9Yy1Gduweee2L0aKYiKlo22CGiyn7MtUiSBrFzcbNSq8OtVbQlSLLBZhWJTVjrSN900J5cuHyd0VPFuBTnbZsWP2QtQFX6Gui4kysw6qv0Lm1F48BX48VdKFgK6hkbyX/TiAvPddGAIkULFb39WdbTdIqH4OMbd8N6NiIIlz9kfHs6UaDh/V3vln6wi3NkGOySVufLMMJUzk/xt78MMSRrYH05xOcr/mRBNcythLVKghiMEiATvVVVomkX8sbAnSOpw06JPCS+W5U0KV6qxs85yY34lmTSLI0vrwabnrM+D7chDmnKU4nY2daA+qloiRU1v2B9PnTdcGKz5HuN13w8N7PjMc+2FZBfwpA305xtnTNXMXe9ABqMGkr5LbKzkBVfY4t+Fa6Yl4ukQpemdDGbN68ZmR3COF0sAYnCVBO+3L/vXChJVZqd6+s42Snh3vW+zjaCdBszKNWnUGjNoOGuQ9JHda5NQQDEmTlATavybMH0N/74u/Pq0nS6iFtFXp/EuDwmgl9OApx+dQUdtdruG5hXsS3mLhhQom+t+w91/NUWqe2hniYYruLsWaSqpVJallGgEw5a+Vkl9bz1l1+9g2fwCvBic5AQAkVgFd8CmMqwyUfM+Mmb6+UBQgK1HV+j1xGAegfvHMyoHTJweuwt5hhrgC8dqGMXUUHHbbmRDHuakdYCTO8fEcZlzWLuGq6iD31AmKvitirCAPAg9JT/ZCCSilWegrQye7xowzrA7UC67Avkp7ejbpUQDdiin5lOLLF9ZIgLBUboemSiqc90UnRjjJsRjwW10DqcNAPmJRQJs+Utk7lcj6obdE36sYDVtZJiRaPcVZetcZHloX6VToCzi1AP/voeHRje8/+yqt+XAA6L7WtIlqAPmoVJgky9sqbeaJgR1NUBDtsLxDxSopT0kFC+kUjdPweumGIpU5bK29SKdeeVc5v9uxSe4F0d+oqXNKoScvPJc2GVPo8j04kHh7t+DgXRDjDxHukQFQfRulbxsn0oJu9Ia/IGoq+BehkAuh9TCZJhE/dPdl694XXfz++udrGih9JzMG2BmZNBNia6i5V7MVOtFyUzyVigjmKVFDEBAMpwYxXuHcQ6PMf1wFNTA6Btk1s5iJxZv3k9bO2o+buytuyuL+IcJr09TPxZHq5ExYSlJ49CtDlqprPwdVEALpxGKnRiaVYwDVTdfzkBYjEfVR80HXeWbYB90417rO2cgYkG+KGpcDr8JmdRLD0qMCo/tUmhpWZNQTw1mmB+4YkPUb+PsKSz1vmrIc4e7mpin7/yioeXd9AEOn8ZnsJAXqYUMAswIGZOdy0dz8a1RrmWtMmbtGF1K61tFn7N7dPBtD/xQuvF1tNxk5WZ0SZhcNEDAE6k2HcF7j26/tqz7poWchndsWS8owAdE3dWuYOQTDjaX52JoZtkcdW0pkY4lzifm0TvaprBLSKZXlviueJ9WtEnRyuluYacU9jfMcCQ9HDnlYTV87OCuBnmxaTIMu9Ptb7AzyyvCrsMQo5yr5iKPbfWl4aKyb+DiDHxadcwAhcBOgXMHjf6Uv/5E/+RFTXjxw5Ii+54oor8Mu//Mt429ve9j89xOOPP47f+73fw8MPPyzPvfrqq/ELv/ALOHTo0P/0tU/3hNyP+rbbxFv3/C9L0cqVPLNMeqUp9LMyiLDu66LCgEJBJ6sYCVZJ2Y6A00GGHnuswxiBqEPSBsvDVLUqCuPXzjYxJQCdKu4OdlUo4qUWO8x239eNcS5M8I9rMZYG9LmmB2SCIjOzbobAX0UUbGIwoM/yRl5hsF7YXBwZdHjFJgq1RVQLFeyo0Scd6GUJothHt7eEKAqw9PjDYy1GduyOvu7lErTzs9vAhOfITY5q41IhNzQyG9CLlrXpSxWQacSwqq6HlqcAm0EUN3krDkLho04U4JH1NVlcV/v6HlKF5KbEINRzcKDVkqwtlYAJfSMjYqXdlJmIzZHizQQANxylhHOcNGwkoGA2VbK6tBITL3mtNLDCLHRtA2x4bV/0ifFaA3iuuUjcu94s7APuOAy3eF0YGLK3/ZvrGzg98HGyN4AoPwfaMkFaLEWMRJSMNDCKw7iQc2UCg1V0JmcYDNerdclI10oF+T3V76V3XOhfpu+LWWZTvbBAnEkSoX+aoE9/T2sj3bDZzyoZcEObdNjLLu0LemxeNwaLpNWyD46zgyqpVGXe36jLcWdLRXzvn/3lRHPuaSvoxq/ZNSr9EixqBJVXdC3FkJ9drH/EOypElvpIaDfliPyWjA2vBzUi+BSpDDsZZgqhtEisDfpSRWZCju9RijsoIsaualGYH+zfJRX5S6fX8Vi7h/W0hm5WwXRrF1qNObhuEa5RjLX0PmttZCtuowDdBjYJq4UXWEH/31/0SqmMWoDL+UwmC+8l0toZ8BAQ8XOzej5XqQg1kJRh69i1NhjgXL9vxlZbIXbUakKBJ/1cK9tD1K0V8xEUboJIXh5hP4w839oM2iqqL/oRGY53ezjTH2DFD+RB2iUpk6NfgidJh6TNoFQUh/Uuvj3vYYrEfegrkwGlXZe+EIvFFDNuitdMp9hdzNAwEm3HfQ+d2MGVU0XsrHi4pAnMVYHEKyF1iwKkeR8xCbIxCLA2SHF6i+sMQbWpoPepzuxgvkFRMw87m6z8OdiMuJY6eKyt7hu1QoKyl2F/i+JJLroJBZuAtYA9mUxKaj/1rhrF/QpYqJaElnqy4+NkJ0A7TrEeUng0ldYp0ui7tC5LHbRDtRSLZUYDfUfXJeqbpkmMlce+OtE9+55X/igKBSMmaqaC7btlj6jyUra3l4xSglVAjEr5CtKLdv1jFZ33aUaWlAJ1sjtExI//jFCpnX2uSSRSbJTJy73NhoheHZqexXSlIow3WqI+1B3g5CBEn2KdRmyNDAOueSKOJRR3Aj1LcVcwqkbbJgMtCuX8PS0pI3z23s9NNHZ//yOvwCdOnhXhsDVhT5FBQVZU7o+CxXpN2C1Uzeb6SmcOzjeyfNi7bxX7uYYPLUwVnHM/k5Yn0yfNtTMHOgLaDTvKkBqYRNK9XkeVax2vFZN0ogMjjiu0htV9nkwqJsa4f3JPIHDnGLLqyXta9lypyDPBq21YTNZTzPSGuWn8zB3j6eOM7rH/7e3qgz7aWkH2iFTJc/tHK5NqIbfts8+78vOWp1zR3iw89rNbGb3RdU7ms9U3sRPQjJm9cjrWbIlUL/HD6+s41enibKcr1d2e78uD4Dzjg/ejk+LQ3AJeeukhlIoVlCp1pYXb5c4kEzjnPvi3kwH0d1//fKMfo4kpxigSL8kc0WQF9zru87vqNbHi5HrLpDava6eQsMAAACAASURBVDsMscliwsAXS0oCdBWIHNbBOPeoO0TwzaSPtXfkGs45xJ9FO8OMneYLhy06vARMKIv+jVi7pdryRxZkqSiPxHMRFVxMVcqYr9U0kSExu9od9/0AZ9bbkghhVV1aWA3z6yJAnwhWPWcvugjQn7Oh1QPTs/zXf/3X8XM/93N46UtfKr+788478fu///v4jd/4DfzLf/kvn/EMPvWpT+G1r30trr/++vy1X/7yl0F/9I9+9KO45ZZbxj77XImcKu4lzbLarLMF55Zao73nqXgkcgHaCBKxwihA6VwqNpJhM05wLkpEhf24gGr1R+cGxB5CUm5m6zXMlIq4Ya6JVsHDYtFDlYIYZd0kSfdiIPBgN8JymOIrKxFO9nmsWDa2gksae4rQX0YUrCMYbCHwt4zdFbGm9t0QoDtOQQB6qb6ISqGMuUrDAHRWlX30emcRE6A/cXii4OHxH3tFTqkTih8DFyMcxc8rwYy1pjH5aFuVYICjQY2Oe9Xx0DQ0cy78UgGRSrqLiqmgH9vaxCAiSNKNnoJokh0nc8B1sKfZEOpVhSwC0pZFbZm0diqcsq+NtmTqja7ZWQXoOY14BKCTmq1CJhrsCWXRJBc42eite8PHJgfoa+96syRqOACCFenBGcai4H7/RhtnfV8o7myjoAf6is+KNJWgI0P75IbEyqVjWAWhJBooOlYqldGstxSgG5oYx1LAqdEE0M51pfsLEDX9dPQPVX90KqFy09XebW6WQmEzwiwEBbx+qjFgbFAMvZGvsVZtVGdeKLGyQx/0uhy37rm44b/86URz7ukAOqv/WqEXV1txSrDeswIETbWD9xc/GyuQbAEQgJ74CDMXvUy1nPnF/xEeKXWTczHDtBcrQPf7ElQo4yJDPfOFvUIgxCQI5wmD3ruWtwQU+YUZhG4Drea8VM+15YO1dxMISiCr6ascoJvKmFa5FboQoGsP+uQU99tufIX2FpvgWVR6mZDK2BPN6hzFmTwR42LQP0uhrVZTxIHs2rgZBiIiZ5MZ4nVfrYrnLb1yRwH6kBprg3mZGHnQJeyXbeuurrwqPKVVP7bpPN7p4lSPVozsyfTFg1d7I4faAtIKY5wepFVFRIyUWWLvWwZ0v3kBAH3WSzDlpvihqRSXlIBZl1CWHuYEyg4uqbJ66+CSJnvBHRTLFVEvl+ovINUxapec7cU4uhmJcFuaFRDGGXphKoB7V5PODB4WWxVhO3UjBvDA0S1WLwm8EpmP+1ukkLpwjKDkZsg9IsPaQNkHO6pl8QVeqJZFyPPEli8Pep532cvs6EPsrlJ9j/WAa6kHWnPyTuqKP7RW3KmUfuyRydpS3vWKHxGAfv4eqxRfzaJZkKMtK1b7w+hG2LVKaO4E6Jn6p2eJBNu0OIyNWBwBuiZm2YrBAH+4dpMWLmwuQ8ulpgL3i+fNzmKuUpU+eccr4EjPz5OlpIQLLVuU7kfExAjQuT+wDm0q7GYzUhRi9BXYXkAhu8/eO1li6H+85uX42IklnOkx9tAYgAkGJl1V18UREcfpUkkYLLREZPKROi1M3pL6Tq4Px5VAZhAboUYyy0wCWtY8kyyTxFaeNLPaK3pN+LGswKO2aWvLmCbHlOrM5Dr3Vh13JnYj2Z8o8CoJXqnKJtgMQwFucg4GhTGBwvfhucyWyrhxfha/8OW7x9onRgH637ztn6NY5LwziSae/0gFXTUGhl34ZpUy+4BNQtiVb8iossGmlWgQ4G7aDezf8rYrM3CWFcJRtP9Xdl4qyUZWgB/f3MTZbg9r/b7oLPT8Afr+QCvobGnk23gOLptfwMsuvQyFYhmFUtUIOer5yT1G/3KudX/3Z2ONnY2J33HN1RI32GvO4/Ka6nqttH7u57zW9Befoe2ZSc7TrWQrjLA+8LHSV/s1FrZsf7jcGoZKzv1GWzGpr2H85NmeJxZvqqWgHExd57Xv3ZGxYiziM7Y2bjK8F/MCQbmEcrmIyHMRUuuJ8Qd1pkxiibOsnAFBEGGj3ZUKOpOndl/kse8/d5HiPjaoeg5fcBGgP4eDy0NfeumleP/734+3v/3t297pwx/+sKixHz169BnP4AUveAFe/epX40Mf+tC25/zqr/4qPv3pT+Puu+8e++ztYnRuBKDbg0iGGMz+qsAOK5QEait9qqmyn5ZVC81OU5jFquhS/fVEmOGsH+PhLbWRSahMrhGHLHp7pxqYLxfxkvkWmgUX01zYxIfaWNt46tt61CfNLsXHzwR4rEOFcyryUs22j4ITIvHPIg1XEfs+4sAXlXKq9KZuisRNparJah1Fimr1RamsVgtalQnEAzxENFhGkgRYOvroRAv5t37k5ZJJFUEoq9RsvtMXnsEEBYiENmQCGhVhIT1bF2oulgyYKuJHqQswKfxSuWRWXmhz6kHLQIf2Qh2qs9OqhdV6VsYy9h9lQlcU+zUmJkhzVfk0lDwmNqh8r3RcHlvpzgrQRaXXVPJVzVozsoZsrBDJVGOVPeWIqNBlfzdeRWk0eDj3zjdJlUCT35qoIL2dyZw12vjFMY50egJIvrq8gUfaHfH7pPYBASRfw4QDgzKOPYMgkrwINquVGmZn5w1AV5qnVby3WXtRETTjowGDBg62Ys52ABHsM7R/W+3kZjpUxFX1VVY+OIfpKawVrFhs7ki5XKhUcO3MjFR2dlfLcjwqMO/5gz+eaM7lKu6JVfHPEIR9mc8Df1PaPgiYWUWy/YVi1WTGiwF5s+hpcoQAPfaFnXBywMBVWwcY4NKzVwMDDYJpYSg+qX5g+uYYVAA7KmwrYOVIKwzL/VBopOtBin4CNKf2oVpbQLFUR6FYUVE9znzxCx+CZQkeTICVU8LPA+jdCwTob33+d+e0XVvZZkuOtfHhfXyg2ZDK+Y5qBVOlstxTtKzabu2kQbT2JFIzQlXNuRbY3lS9Z4xjhdEoYE5EEmqG/muptOpeoEkgmVvW89tEnA9vbOLxTkesh/jgvUl3Blspl5YOJpRIuTSuBQQxlpbKsdZ7JMK/+9rnJ5p3uw9ejywJUXVS3DxdwO6SgwOlTCykVnsJ+hHPOxCrvX3TRSzUPeydmsJio2ENEnCuH+J0L8Djmz7uOdeT+6biUcTRlaphs0w6ZgmNUgF7WjW5b9OMVfYMZ7o+AvYQ+yratLtRQaPkYd9MEfWSq/3kaYrHNwZY7Uc5WGPihMc72e7L+sExYgKGVfpGtSgAfSvJpC3rVC9BSsHCUk2ERLeYuIpTPNENRJTuH+6dTNjxjS/+ARRNBZ16GZKUkYmia4wVZbTXa3tryjCBqGt6pgk46UtXloto2olHvIqAMrkqiTLSgw3FXIB7XrGLBRSwusv5eNXsDBZqVVw+O4uFWg1r3GOSDEtkbASRMOHYrkaATsaBqKKbvlitFGuCVUCrOBMQhajLA9fNKI3w8bsna634ux96Kf7++Gks9X2ZB0wyaJXalXPm//cx6V8uCRuKYyFMHmExqW+22NeNjrsB19wzCEykR90kW0tFpkAgDAtJIopkyTB5aEGgFcFjEkCBql29NIqyyU7tRWfswkppKoJeTPARlNJi1Vo26phqSwE/J+ftdy/M47fuHS82Gd1j//xtP4OSAPShLgkTTwIQ81YiA2zzSq0VEtWwTW3D9Nys64Ro3BkxW6txYlKsJuFoggUzCvl8NkwRzkWO37leV1ozVnp9seGkdeAgitD3fbEh9P0e/EFfQTFjJo8+ukUcWtiBlx28HB7vqQLFMo1lYh6xAEEY4v/+yGQA/TWHDgpIti1/VmOAt6wWKiBsDc49Jm9pp8b1loCdMTMB9IYRjuO19Jlp1Zte5iSLTVbFXRgTUiyxOkbaDsn3YDuVtO5JHOgKS4R/O97eknkriR/efKNMOYoFV0qolMvwPaDvqeZBaGI6gn4mgKuZA5euMz4TpRRy1GSBxKdpiq+dOjnWPjE2ALn4grFG4CJAH2u4xn9ypVLBgw8+KMJuo1+kuz//+c+HTyrPM3zxtQ888AAuv/zybc949NFHce21137b1z7TMXOA/h76oG8XiZPerixDP6ZSLgG60svXZGMJJWjig6qy3BS5aQ8y4CwrHQEBeoTDW4Eob3qJ2lQUCvTzLuLgdAvzlSJeMtdAs+ChYSxSbIkq9pSQfdKPsREn+NjpAEe6idAcSX1vFAaougGyYAWI1hAHIeKAYhvsK2cFPkXkJjlALxTqqFZ3wmN12tPPGTKjnBLkrwqgOT4hxf2eH36ZVA0lCDLATYICUtsoSCQUP9MDZzZ8LoAyviaNLP6tAtDdHKBbX1cBk2YjFLqg2cD7obV32g7QCcClJ9YAffZ+CQ3Z037NUsFQ+SQopACOVtEtWNKqr9T4VIFehUCNv6i+RgMSR3o5D9w+XkVpNHg4+843Ya5CWrUB6JLd16o0LZsYcB/t9rDih/jS2VU8tNHB4+0OTnb6+posE3V7ih3lPZKkYcJFpVzF9PSM9F1KtcX089uxFPD0NACd52eFbayInhUBsoG07TWz/tkcP+lNo+AYx0roY5kC8loV85UKrp6elsrsfFlp4dxcd/3HPxprA7T36wdueQce6hWFlqsq7hn8oIuIAH2wgTj2paLG4N32aJZcBvWa0GDfP+n2Im6YRgLS21GC0wO2NGjShwCAwo1aHdLggW0oIjxID2WjTM/5MF9lYKKJH14Yenj3qE1BZe3UwezMfjTqOwScF5ggM1UTqcqJOJcN7jVC1NjZVG84MY2ondqsXVgF/ceveqFRcx6KtrESrW0LWm0ktX2mVBKQzvWK4JxzaHi9TVLG6EoQ8IlgEO9hjoEJgLXuPxSOsveR/F0A+lCwzya/cusqsQWz1n/AY+0tobnTqoc0X6sgr5VyZcMwSUCgvg2gm8hN145UKJG/+/XJQOa+g9eLbgdTnC9vFbCr5OJgGWi4wEYvgh8RAPoi3rl3uoT5egF7Wy0sNuoWq2E9SLDsJzi+FeBbawPDmGDlSBkrrIgvNkvS17nYrEpgSttMnvtyjwA9weaAvZLAzhqrvx72zxTRLFPbQ4HQY+sK0KVH1PM0wVIsYIk6Fp2BgDfO/0algJlaSSjdBJ+dKMXpfqoAvVwTK84uPHTjFEe2fHSDELd/bTKLuje86NW5SJwVHSPTS9YbIyJqK+dPAedWjMywfEQsTlp0NOkmAN2wMLj2kE1DRXcRvpQKnVbXSXenmjqTcGx14Jhuheoectn0lMz3K2ZnsaNeF790CqlS82M1iER1nICdyQz261thU6sGPlpBTynoxfOg6r6Z4wTon7hnwgr6D78Unzx5VlwNmJzmkkAtBu5z87WKJP33kOJeKglriONDEVqujdJbm8Sm9cnckSMVXf6d4pCy7pGRwHupyNWTLQ9avbSVckPyUbBu6MrCdjHJa7NyyTcLzvk/VUbX+IAMEJ4Pe49J6yatWBNnXHuZgOeaTaaI+mizgv679z821j4xusf+1xygaxJFGAAmcTukuAtyVFaPaYUZbTViUoygk+wD3n9a8dXEC61EmcTV5LUex3zTtdwU3e3cthRtBegplrtd6Z/eHATSotYzOiADn24PPiJ/gChQ6zKmFTyKslYq2D+7gJsOHITHVilPq8254Jw5+SAM8Acf/Yuxxs7usT948KAkPDVe4Ge07Ui6Q/Gz7qrVQSs1AejlIUBnspdjReszepPblgaD0HUdztsGlaVGYG/1Q6QdwyRyrQsI13XGGE2ykVwHJ7c6kujh/LVip6a4L29Toi1bqYiBC/RcZU0wnrJ7AFuoCkkGh/fogO0XmrzNAXqS4s6TF1Xcx0d5z90rLgL0525s5cjXXHMN3vKWt+DXfu3Xtr0T6e1/9Vd/JQD8mb5op0aK/Bve8IZtT/nrv/5rvPe978WJEyfGPvuc4v6e21ATNVEbDOsGzGCVSpas2rK/jb+jQjs3mBJpxLQMM4E0xdyWohTH/AwP9FNRdj/ZDVBBijknkeCd6u0EZDcsTGOhXMSNM1URRaNoCGmcaxFBWYaOZLMhVHn2C37qrI/HezGC1BOl6X2VEAvFCJW0g2LaQ0zRF6koKwUvdBIMHG4/9MSmQnMFXrGlP1uDMdmMIiDeFGXhux74x4kW8i/c/CJT3Ta+okZUR3rPjKAO2QUCQszWbtsB9ILpRse4nkwEisOowrpanPGLmyEBhCYBjJiWocAyxuN1C6EVdAYoQpUiqDJ+v9JzJCDbQIycqcaNRvvPjda3Ur2l39FWCNWeTPqrTTXfWnvRSm/nX98x1riNBg9n3/VmzI1W0M0M1q2Y1woiVESK5RfPLIsP+j+ubMh3MhI4rhQn5IMe8yXSM/l52OPMcWTARjaB6R0js0Dog4bSrgB8SP+2LWzWu9UGWbYuYmlmvDYMshlsiHKvoYQL1TRLMU2WSLWCndUqrpmdEXDH6o704coMhlglzf3HySroH7jl7Xi4XxJGCav8rNz0/I4A9L6/hSgOkCbaL66+94lUwxv04yVgIW1fbPzIXCG7QoWzCETIiuhGGliLqJ7xVVXVdp0nnGf8e8XT59jefgaVTJKRXsz7MBBjMBc7Z/ZhurGAEj1cvWJu48MWEHot895nYmaE364Bbi4cp8EXAXrnAgH6yw9cKXRSsdTjpzAVAwExZs2TXkIBjBSeoiI/AbpWeVVlWf9uq54Ef/S75f1Bf1wCHlLQc6E+o4hvCZi8rwnK9J7UWSfsD/Hg1qBQEqMiLMVEWYoN0iYjWulQjdxoILBlyAgJMcBj8CaV6IK6MVDFWwNvY/UnytUR/tNdkwmdPe/gdciSQOb45RUP06SaVotoeS4GAx9xxLmmFfRdzTJmqkXMlMlCoP+3JmIcVru8qiRbN0MPq/0ejrbXRIV7nkkOYVlpa8k0WyaKKmjGtasXKkBf6pDBlUnihM/b2WTPpoPZalmA27GNUATnhLnEKpE4YbjYCmJ0AlbWPcwJOC9isVnWlgfHhZ842AhdAehpqYrU9ZAUKuIm8eiWj80ne//f+7eTtaW8/qZXixWpVMwFmGuSRwGsKsdrb6tCnCH7wjxnRGRLQKh5KOCxTSCm8il7g90nTJJHsq6pJKMJ1CnWx/m+6fsCJlgN5DpF0TjO5alKRVxZOglZMNSPSeVBq7M18SNnlZ5gTZPPokydaHWRSQHh7rA/XvYRTxIGn3lwMubG5177cnx9eQOrfigq7OL/LPEIhSqZdPcwV62hTpV8CxJNry6TiQSAsv9aZpiIwqmNJO8t/RzK3BKNkgIBOnUP9B62NG4Lu1VJewjBhQ1jWtVsEGavH68n72MWOVTQNJP7YUelYq6Rshpon8r3ZwJEKugpk6lFXDnVvCCA/kdvfSfKJe1B17iD9qTWB11jEtvVLOtfCmmd0Z5wpeTTieRsry/rDvVvbIVfmX3qkCIglsUa2SPyjAUyM3g2WcnEibREWpYh2QtJrCJybCmhDkgYoNvvoj8YoJiGKCahtD3y0Wi0MDu7gJlGC3vmd2qrlG0TMq4WtrjECvrffvpvxopPbEz86oOXytrCs9IWOM5zrYJzTSID57vm5sRXfGdNK+hcgxlr2L3LbmmGVLKNHSY1+BHGgib/NQlg2U42gau95xoP2pYpzmkmc+yepZHk0N5TbGjZ248UPSbqja2eJolovZtgQNeSMEK72xOqPItvlh3I53326EUf9LFB1XP4gosA/TkcXB769ttvxxvf+EbcfPPN2/rIP/e5z4FA+3Wve90znsEHPvAB/O7v/i5Iaf+e7/keeR570H/zN38Tv/iLvyi97eN+PRWgm50+D1oz2Ti4UHMR5aLKAI8bChciAnTeyNwwz4QpTvgJnvBT3N+lWm6Ms/0AdSfDTi+VyhKFNAjQb1yYkkritVMVCcy4KRB4LgeJeIRuMpBMMxGIo13b51d8HOsz4FJC36XVCDtLMapZH+XMRxpnSBMG+rRTcRSgI1G/c8JNpwTXo4Mve+VNv5XELzHSZEuUVe+8e7xKsB27T3//jVLBko3PKGxauxlay6nirQYxeebdhO42gLeVNbIROE6WVq3XUzcGqxzLY/NLFchtnzH7AGOly5t+uKKlypveO6scrEmYUeipwSG3IG4OYq8lCtC6MQ374Al0VQTFUsUpILR4AQB96V1vFgq4VtCHAkk8O9u3vxrG6MQpvrS0goc2tnDP2qb4oUs/pMngi9WJV0CZwb/pQefG7ZJNYACM/Twy1gags0dRwFIe4Bpv17wDL2+llN9YBXRro2MBOmurhEI8XgUA7dkurdewo1oV9W+OG6ubnHmusSq7UIB+uF8SmzVWHinEMwh7opQ8CHriF05LKAL0JGUFLUHTy1D3MtRcyEPP3UXRy1DxGJyq1RSpeAzC+Xlt4K+MAtrWaZAgIsqkkbIFQET69Jk2eGaVSOiQkiBzsXtmD2YbbDcoynVi4MD3Y1WuLQBdNSdyHrS14Ml/9+wB9BfvvVzn1kiArRV9bUHh7201W9c46kAUBfgRVMvDJDds/zevLUENn8+g2qrzajiuAbANwOyYStBnVKP5d1sFIajmezBIJr1R1lfpmdagWfQPCCoSrbqxQsz3ZbWHdHxN9ul5MniWe8kIUUqvaxzjj+6dbK276YrrUUIk/c97Sw6a1LygrRV9pwc+EgKajImhGAt1gjy2UtBKs6gAnfdaoYoC6eNZAX5awnK/i8c2VkCJ0hkmfAzdk4KXdYqHFgvYP1UT1g49kLkXWYAu4Iy0zwqkxWJnndXUAk5uRdgYJGLVGabqZc55yMQR/dcJ0GcqHmaqBexq2qSeizAlnd0TgJ6wFcPxEBfKop1ypMNe0gDvvX0ygP66G18lCSpZ66TH2QDzERsvK8AlKZscsOuabMG7rtFDgK6tEQbUS7LR+k0bbRMBFaQHm9kYUxSSAqms7KnuAucSGQsEJKS3kzXCnlompriHESx2UxV83QypL8O5yZYG0uRpL2UAOsda4gEV8eQ80ASEJ/HD5x8aL5lr99g7fvQVuGu1jXW2PZnkABk6Iu5GsTiX1F9V7tdPqSKNIgYrAF0ZPwo+9P6xYyZikRTBM2uCVDNZQSerQhIN3FNtkkT3TQIcvV/1Z96fuZBXDrps8pcxifYLi2AaxTZZda3WpEWLewLjKRY+BPiaZD4LFBSfvaxVx7+7b/IK+h+85adRNvpCuYq76Ue3P8uno2AYFdMTUv7ZokTFdI07Vql70e8LbVuuuREKlGSGSSjq2qgAfZRvLQDdfOkeopViawMrSRyKHop4GZk4PXQDH71+FwO/j3IaoZRGSNhG6ThoTc1gYX4nGrUG5qfn5L10yE3V2azjvDxhFOJjn719QoB+QD4bhyav+hs7Pnqhk9p+3cIC9jTYDlWVSjqfLyyy0YRZzgizs8U2QowCdKs3YVO4ZFmoP7pNfFiAbvVEpAdd7F517tlijiTIhLWpCademqAjThVMvhs3HrH2ZVErlnaC1Y62GVCcb7RN81NPHB1r7MbFHxefP94IXATo443XRM++6667pBJ++PBhef1VV12FX/qlXwJ7zL/dFzccKrj/9m//Ns6cOSNP3b17tyjA//zP/7z2s435lau4v+c9YtellUvN4IkAmfSuabbY9vxaUQ8BK64jqu2suD3aDfHgVoCjgwT3d5Q+RzrdlAfsL2lVZEetKmI9186S2u5iscSgl4uRiqQQoFOMZylIJBg41Y8la/9ghxY+3OCEaIT9lQQ7SlTGTlF1tJLCIFoUvD1PFqYApKwBXQHlLpKMgW+GMncGod9pv20/CxHEIW7/4kfHWozs2H3k+14gIDan+plMpWwQOUDXAMYG7DZ4sH3+tt9Wgxlr96IXk5snn8cFV15nlWNNfGC7dm12Xr1YdWFX6rzSs2zCVp6f04aHKtL2ubYfXirP5hgilAYHFDvj8RtUiXZU1fx5Hx2vGsdTsWN38p1vkj5orbIM6cA8xRUJEmI8Ij3oAe7fIMV3gHNBIkyL0HURGSskirRJR7qZHxSW4nmyj1oSCgWOBcWCjHq7yYjz75L9t9u8GVujiy+gwgZ8PG8R7pMxNYJCxt+WnrUN15GqCFXa2XN+WbMhGXXS3A1DWz+nycKTLnshFfTD/SK6sWpA8DNozcoGjRqQa0JAnLVRzFj5VEE4PngtqflAcN7w2G7hIXOKErCf6nZk42egzTGaK5JmTPsqjhSk/YBBAecM35HWRdqfqHRO24vK+48/Xza7A7uaU8Zu0AVJ0r5TRi9z0aEPtgR8Sp/Xyp8CW/04JlXwLFXQb9xzSKrUqi49VGK2S6esgSO/l+qHBD2swGnwY4G1vR/F956CQbQGo086K3xCLdZERR6wWQVdmYu2z1OtJylIx6CPlUCqsG8EgQByBmKswMkamVAcTSntrKCQnszkQJN98kaojs+hQJEGrGr1xOcysSc9j0mCvz08nrCjvV/f89IX47IKWT4ZpkXlP8PAz5AwyRJwbtFZIRSRQtp70RNeAncmaqgrQq2GQkVAepHqy6U6tsIBlnptxGGIuNcVeMl1R2aAE6NacnFopiaWfkVPgZfQRuU+Ksj06P1/7L17sF33VSa4zvt97kPS1cuSrIftWLIdJyaBOBAnkDhANyTdVMcJhE6aomFC8ShmoGr4g2JmqgYKpqGoMMyki+6GZKCA0J00CU3xMIkTJ7ETJ7ZDLL8t25IlWVdX93Xe++zHzPettfbeMsnE59gOXYWu69a1pHPPY+/fb/3Wt9a3vm+iZ9ShTk261ZJswN5zmsjatCjb0wIF4EBhD6TMIm+tUpRaFaMABenWnZ6rQo8FlApg/VesspDUQ+EpjuW50YTF6j/4u0/MdU6845bbyfBRJq6CCQIjjyW5Yo0XiC4TAMwp/tOBwq0S05/aB1WJsUzQWidZlQbPeEBwaurutHdS8IguOq4h1haKR65QzeKnWUeCqYQu6jDEqNtEzg3RVQXIR+cVs7Vg1NmMLotdWujDFwD6fae+MNe1AEFl+AAAIABJREFU+/w73ywnN/q011wDBTqKBSokYOdMkpKyJKw0QcBijQQAaXwufJONYgAd4EQ3pcUauoiIoHwCKnEXgLaAGXR99xDCVecLXNtEhpEWN9SfJaEWRKrozl/RTqufFcpWKtAoBUsboxyLlSpBEjqXHBfAXDBt8fS1UdTH3t7fqsv/9pX5Z9B/+93vU691o0zTS9sAugNpMJhw75+EMOtgSI0RADYAOoB0nPOqRaQCuM5b1/WpgFTzEC1Eah6pKDU2WpozCgBi8RjV7CjxnISjCbWKolie2bgkF3o9mU6GMg3GUsUZFE953geFkiwt7ZC9u/dLEWy5WiNlMfEdWIPE7wUKhp/77Hz79fuOXG3FhpSUwfMI9+iWlRV2zm9eWZGr2ura4ew6Z7/QGo1aIhb/cxZ+WTEhl88ZM80FhFEgwW/iOpG1ZRolfk7hTOLZ5PvWXH88l/R7AhX5C6MhVeUhVMdCbg02x2XZUYPd2lROb2/L88OB3H32LEegVLMilk88dQWgzwipXtGHXwHor+jlffmevNfr8ck6nc5LetLMZu0D0iLFHQeD5sQYI3NFR2xYCE6R8epq0dYKgl3NBgB6byJf25rIM6NQHtoGpQyBPCRAP1jV+ULMiy1W4ZGr1PYlo+fSvgozhkFEFd7nzZ7tuRE81RN5ahTSascTm4N1kV2VRBpFkTrVNGu0iUMiAYDOWS7QxRKRHqldqGKrGnejqOAFljkIcn0J6f/4n+/6r3MlDx97880K0G3OTBN6pY6hK6jzZybQYpjDryse42BAw20mxOYFF6YExmhw8JBvgitg166405JxnxSMmhCcdRQYtA385MWI/HFeBffOsFPaVXhHxQDxvG0esgrQb/rL2bpxeA++7p79sTvYrdEOutHPTdUYwnC9MJSHt/pyfjSWx7YG8txwIhthIluwnioUZWLWcADkuMdI2FyNHu+v9gKArurtCtpVkCXrRlFxNgfQkVdwL6R1LzAIVG+BVkcUaYoJUrrloiyVS7Kn2ZAjnY7sqNUI1Ek/tg6Cu3nzHSaYQQ9k50uguAOgo4PubAZV8zfQZ6J7oNyDGozXLMUTfkcROp1jBeecy9fOOsFJoSpbwVjObG8woUXHsl4U2VNDl7bA2XV8DnTRfLadnaZQ3Qrw7UKDWqACKCzIsSVQATtaQCmUZFxsyrjQlCGV4wHQdTTFAbrehlcGoN+y/wiFA7UICREoY29YYYswxrpvnvwAxChTSL9V+E0fh+uADjYSqRSgg41g9Nh/CNA12afKP5M4uCsU5OjCArvwO5stUo1hzYMkzV+bdFNQejmTXSA43xiPCczR7QSo2N1q8XUBnjQvVICumiEZQP/E4w/MFev+57fcKsebZQL0FopTcSxrvZCz59FkKjF9eKciScQupPtBoyACcI7vYqlGkF6tNKReb8sgnMj6ZCDBeCz97S0yg9hnJg03kHqlKFcvNThmg446vvqhdhtjgLO4IJcG6PYlcqhdkW61yNGPcSRyflKU9WlB1kNhzIDhIb6hhVKBk0VZpEWGu48rAIyil1+SBHPveC0IcMaxXJhgvCCQ//q5v5rr2v3Aa2+n3ZUm5wrML5s1t8KsaxakFGlbnwoAfFQJxXEraPpPo3R7t1eXlxsUZhofiAlOf+ceZaHaxWAxD6uABHHF7T51dKNMdgKAJACvA3QwEjbGAG8F2uX5LDryBAfoCiRCuf/p2QRF/Zz4wjvfQoC+Pg7k7BBWiLGEsO/DGRDjvC8KPdcBNKndAPYE9o4yULh3OZePmXLtOmZ8Nk2hUNiASCvOCAAYoGncf/6bFbkB0HHrxqBcEyCBJaCuHvRS92oETiOsYzLQdDSMTCcD6Ogio8iNGAstABZDI7hiFJnH4DwD9wBFkv3Nhvzyl2dzmMmfsb91x/ukUcOaVv9tvEUH6Hgd/Bkz5rinj17aYBcVM/kAbnrtVLuCtH7+v35IWOtqwmKaG3Z2Mi6aBgczE58bY26DQoUC9Halym76iUWMO1bZyQW752kD6GEwlmg6kQoAehLqeQ9v+IVl2bNrrwjYEhUwhlC4o3qpanZY8QHvDuLE99z9F3Pt139+7PBlNpXaSVedle/Yu08OdDpyw46dsrfV1nPBiro4E3Tvqign1ob/2S0wPd+yU06vpzUDXL8oBei0DFTWltPb8Xgfl3S2JuIjCh7O0nQf+jWOJ0DHZyzP9fuMozhnupWq7Gu1OPd/ut/jYz579jkWhPEceN8ff+LUTNfuJYGRK7/8Ta/AFYD+TS/RfA8ABeubdbjx77CF+FZ++QG49lMfsBl0pZozwHKuysAM53b1kPfqPjrc6Cw8O5zK6cFUzoxCzomfH03lqR4siACXptIsJLKjDFXoIgWq0PGDDQ9td0wsamR0O3TjAWp7UZnUxLVpid60fbN2iSFolcSyv1GWHei+e4e3UuHcGK4zgh8t4SDGgjkyFnzR4cacMt6DhkJWbCGCB+uT6VQ++6W/nCkY+bX76G038wAnIKfNmlZOCQCskqxdRauoMlHWbkUG1E0UywFhTmTGK6LeOXcqrCHtdM7VmWTamTOATi9sTz418fKmZNapdzV3/akK8iZsZQmhihIpQMdPzMkDoCKIv/HO+T1aH3vfu9hpw7Xyz4fXx9dGENKD9+nBiCJFZ0eBXApC2TBdgkEck2Whs1UKpgEGXZCHyWxRK94+NkBAniqye9dexwXY3baEFp+TiS7ddpTGrR7E6EhDAA6rKaF1GujsB1sNOYrZzUqV3XNV39fP8Q95Lfo3mIXd+X/93lxrTmfQ0UF3kTjj6mvWr/TZRKmDPucKijC7bgkEnCL+P4WmCioehw4U7u0wGMp6b1Um07Fsj3vSLIkcbulsMB6PpOPCGFRIjLEggYPdnq5v5ZFkVX0UprB+D3aatLpq1NpSrzZlVF6UcWlBplDnRnJNwUZPmTMP3qyDjo9VfFlm0G/ee4TCRvjCZ8H7c4VcgF0UoDwZUt97dAxD6yApQPc8XOsI2lGnmrsBde+6p9R2LgTrcGKW0GiYoL4+199ign7rvj0UU1uqN6VWqrCTBREgxpQUZBj7RxK5OBzI+UFPdtYbTBZBTT62tMjE+nx/aEr1KBwqQ8nn+/F5/vzxB+dad//u+26Ta5olqRViKQQBKe1PXuzL1nAqPShsg1kBO6Qklh2NhnRqVdnfbcpKq06Qi28O0BTKqrxcaUoZwna1qvRHQ3l+7SKyailFgdr8lWPeq24T7ISS7OvUua/iouptjMOqTKOinN6c0Pt8uRpJo4waEEBOWR7pF+TMWOT0KJLzAWapCzKkzZhas3E8A7k9hbyUoYV5aYBh/PRKClY17EIxR33yoc/Ode3e/uq3Sq2s3sZpsv2CbrrT1fOjD5xLNYo7/94Sdd/XSpU34G5icZ5DOEhP45C5iUAFHYACZ4nbNbk1aMrQSmOhKsa3MHJQ0jEXjLZwXjqByBxG2QIq+GOsABoUKI5wT7vmCvUdpnLyzJfmunb3vPO75cG1LTKpHrh0ieNzS822VEvKdsAVQpEdbjMaSAA41XXGO+daoFJVdmW2aFfdrw2AY6ukeUS33mKhv1av8ypo1zjriMMRgOefUdd7dLVRUUc0JtxSEAAbZ0+zrHapLJZaIR1nDjvnZCMpkNPCHZgBsPWLWRQ53G3L/3jPQzNdN3wmz09+693vl3oVAF2LzYhNquIOVXnNWZ7e6svaeCynNntyYTDidUQ328fCyCBC3AMDIQyVnZWOCBljI8/gNJ91DZR2JvjAFGfWMZbSIOvx+oWOLFer8ny/J9uTsawN+hRQxTkFRwIIueInRtoCFN2rNakjRlaq0qw3SN9faLe0HGU6ABqfEwmmU/n85/7bTNfOr9t7jh9T4G9f2GP7W23pVmty88puCsMt1xtkb2IEALEaYBf7iasftp1BwCKqrzGwqxDjUcxdqtYZ2zjShHjjhXzLHUFJx+fgOEDup+dw2L++prHXUNQFQNfCpY5l4BuMOLyHxzY35EsXLqTFlqVqTQ53u9qkmqpOw7M9Zc4hRuGc+MOHn5jp2n0rscs/xde6AtBfobv+53/+59/wme+55x754Ac/qLSsF6i4g/b+zYC9P/FLsVm79FM/JY1qlaHUu2A+VIXEXDd7VhlEkNicxhSQeao/kaf6gZwbx3J6CIXegFRkBNhSISClfKGIDrzOLgMoNGGBRrVwDfzjaKxzm/QjRsJflTApyVZUkQk6bA50ownn2g626rRpg0UPAJiUSuqFaxcD1fQYVWF0BpDTQqW8UGEi3qmh/6kHNBMU6xTe95X51Hn/+E2vNisgVcpUIK4HoXbSDZgbdTabhcsAunfGtReuIlyuOZPZTWltNJ2dtcZ5fo4966BjFiybcfW5Ra/SeuB39dW8SrlSwZWyRV9w7xiTeqqd9IYBdXzeN905m7hePnl4GAAdgjycD7TKPJOIArvnoDKeGQWyMQ1lFf7n04iq/tvTSHpRxNl02uOYjQ0ZCyyUKDLG/NvlFib2uVwszrpF3tV2v3Ol8tusOp9Du9AVA+aYdMT3vnpd9ja0W35Nt8NZW3jxanqQEc4v3/ya0GxPprLrJQJ0dNDRUWVRwxIkdEX4+pF2Mr1ohW4mKviYgVRFWp+RVBVit26CGvxG74JMwrFsDbekQYAOgTQ8qY6iXBhjBjWWzQk6KxB0VGheKSDJTCQI1QEASTzu6lVtKNlXpdNYkGa9kwJ09FvZ/fLuA/GuDW3YT+2qv3wA/TX7jnC/4lXQDcLr0VsWFl/VCsV/eM0IynTm2xWXlfavXfe8yI8XfXB9sZ6ZluaAe8aG0fWHL1XBjuTU1joFIL9z/x7ZR4DeojDd84MRWRaeiHm8cgV82BOd7fdkV0MB+p5Wi17WeK/n+gMtVGEm2IAYSTaWeH1iToD+6993mxxuFARcqziYsKD81GqPAH0wmtCmB4VPxHWMMyEhPbLckasWmqRGA+RqybAsMTqglYZUq1VpNprSGw7l7NqqSDSV0nRMxlazooC0Ui2R4n5VpyG1Mmjy6oc8DGu0+3x6fSL9SSRdCIeWYimW6xSK/FovkWdGIs+MIp5PKNYOMUrhwpjW0dQRIk1MqahO20sTgkRBl2BMxc9OPTobY8gT/ttvcoCuRUNngzF59yQ8p0uQp8y6gnQK3K2AmqvHqT5GCtDTVq6uNitA4m/BVsH6hRtKHqD7SJsLbLkeBMwFcS+gMwDtila5SMYQwR496iO5OFbXhrUBKOUYqdBCtaqGo4gKwBfKI2fnB+gPrG1Rxf2LqxcpmLij0ZE6RUFZgpYxi/wK6PDeFZjb99cpiGM/ocPu1x/gplOByGBZFhptio7WGw2eRRAmxLVDoQLnH+zPUJAbx2pBuzUZk7kS2TgguuU4PzBDDnCOUScIaZLRUFCw7x1fzq9bHoT3gvuIe9CPIuoCHF3oys9+/u9nBkq+7n7z3f+GHti2DLRjTgCtzCcU7x7f2JILw5Gc6Q3k4khFBJFDOQPAYw7+PjaADvYMnzM9Q7NSdD5PccFVFFKwXiIbS1uoN6kZcA0L22V5bntLNsfwuR/LMECBzuR8SYsA+wXfSPXK9HUHQG8BINfrsrzQVbtYL4fbuhtPA/ns3fMB9H99wzXs9uvN0hGFIwuLZCvdsGOFBVGAc4xErA5HVOOHICFAuhc01scjWR3CIk6PZwjJHex0OcqEQj7XGlTpybTLmAiaO3pmp9mD61L4VWY8yjV7cO5gTXo333WMcIbgmj6yviFfeP48C82DIOAaPtzp8rnh2oK9gG47C+0FdRP48MnZRyteIQh15Wk5kePDG1cuxyt+BR577DEKvn3yk5+UH/mRHxGIwB06dOiy14Vn+ov9+pVf+ZUX+9D0cVkH/acY8LA52UEnpV3BGfITPVo0IOvBl8jZ0VQuTEJ5aGssJ7fGshHEcjFQUTkow0KgCh1v/CZNS8jCVvITKqI8uGkFg+oxAkPEQMHUotjU+cJSS6QABVKEZ3i6DmiN9m0rK7SFWWg0pV1ryLODiTw7HEs/CFm1TDD/OQXNU7sEgqptuUE/0G67zWgJKy5W28ORxGEgJx+az3ro9954IwOjdswVWPuYgGFopbE7UDcwmqe5O6WWnWSn2BrNlp6bOeuTfIdDL5YJmzG5A63O1VSzzrgqlWsl1lO3NAG05JBFGAOm6vutHVbMnmvCkc2gY1wBCSxAyi1/+cW5k4eT77uD1WQtDGk3U6+dqviTemkK30gmwNjYnoZU190KprINWxbY5ExDUyFXBgZmn/H7A1NPQSHHxbNYjXYVZSs6EaBC4M2o/C3Mc3HtW8fDRI+6UCyHMFazLrtRDW82ZE9DBWPQfdXXyFSVMwEbHtG2714egP6wUdxVUT8DfgEEdeJQLvUuyXA8YIUeyWe32ZF2A52hipTLsBpUAizgJJXck0DqMpTBeCCr26sSQHAOiVIhJgUe+w8JD+IDaK3slIW6rsE8RjeyW9FZuQsDUCShJ4ECisie7rIsNzqyo7NTllqLMi3WZVqAWZcl8S8A5Zq/WoLi1ElwcqDiPrwkwXQkdz/yNzOtO491tx6ETaWCFKcjolOCa0igDgaOvbbTf9kZseTbu28pUI/R5YXncjarqx45UGQvkhbMuGne0OheMsQVAJYieWZrg2JwBzotulzsaXakWalKL9D5cxUCMlYTuyPapVsbDuX8oM/nR5K30mzKNUuL3EdbqbWP0UqNhuoA/c5TJ+e6dt9/y82yoywC0FYE2Ma9mURSiBLZBd9pALbxRIIpROIqslgry4ldLTm61FCfeQAoOixUJSqUJSpopxvlrv54LOc3t6SUhNJMJtQ+WKiBBpzIuRFIuYl0a2BBFaTTrAh0J7bChozRQd+OpB/E0qzA7zqWkRQpUPi17VCeGcayNhXZCDOGDbVKHN0W1QILFF/tbquPPRkl6BjXVEMC5Gisv6dOztdBv/3Gt5Lirq+hezb9Mm40u2UGvlUUzubj/f9zdlZqzaZxhTPpRml3wUtP7/0M0iKYCkGieIMCG8IjEnE9i0x7IUVyVi4mdTumlgVAEwUxwbqhtZ+ub1BmQZPeGgeyGUzlqa0hgTpei3VX2N/FoTx1frZiru/Ze975FvnqpZ5cGI7lSxcvyvp4IhN7btUuwblJ0zAFjaahkwEYO0NNfgXK8xTNQmec9Rql8O9vthjH93W6vEdRSQuPKELiS5sVRY6TIE4AlGEE5dJooKryjBGx7KjXpIPRk1qVFqBKdVbNG3qyk6VWzOhsrMbrCKE5WQqUHADQD3c78hOfvX+m/Yr36tfu19/9b9QH3cbkcE0uTSA4CfYOutWBnO8PGDMmU7j1KAMBYDxjYJhuAT5fGCqbjPsFa9T71b5wbFWbTzos8ABED3QXubeeGYxlGMVkbEIwtGNrarXfk94EjRq9Tqoq46emK+1kdq/UFSoUZbHdkkO7d3N2f7nRSu30OHoVTOSjd358pmvn1+3HbjzC58KXazC8ZmWv7Gq2ZHerw1EEdcGE0xFiNYCvCnmiaw0WwLl+X073ttOzDKMTKMK2y1UyjGC/emLnDhZ1fdTPrT5JmWc+mWPiuR0xC2zKAEVM9dwJ+xgFWhSWud/jhAUDjDidG/Tlia1NrlMwPlCIhpsA1yIOb1sfHpMQr//jQ7OLE84MQq78wou+AlcA+ou+VPM/EAJvANMf/vCH5e1vf7v82q/9Gu3X/jG+Uh/0/+EDUqvAn1gpT14tdisXBGJNDGHTEzHZOj2cyrnRVL62NSZI3w4TzqKDajYMxgTcquaa+RmzMkuAqsAcB5paQQ35c4q/R4+l1JJioSylSkeKRRwu2g0Mwp4kcSC37t0nr1pelp2w3Gi25OGtoTyyNZSN8UTWUAEOA4lDeOwq1QiUx1K1pXQoVA0hMIZODrr8wZAA/ZE5FWY/9IYbSFVKgbl1zjW8KqfMO+JObXcaUjrj78JUqR1bJlTldHkHDWoPlUE/PURVfAagO50bh8J2LsHTw86ymVxF1hPBPEBHp0ABugPzDKAjSUEyg0ML7+TVn5xtrjCfPDwEgM7kQT1iyTbgmIACdbIRbK4cqv5Ym6pmDSGsQHo4fAjWQYePZQsJBtRoIdIVi2wyAVMhG59/IziyajVVda0zgs8P8A1g3jaAXrWOcDEOSRnHnHmnVJQj7aZc1WzISqPOb9c+cGCpHbHLAfnLCdD/V6O4q4q7dtA94R+H8AqeyvmN52V7uK1iPKWSLLUXCdKrEOeiQjW1oJlwQ32+koykFm9LfwKAvsaEEkkHhaXgEACLxXBCS7QgUttAqDfj31sVJOwJBbqwZp7rT2Q7gNCjslj2LqzIcnNBdnYB0JckLFQkYuHNCJBpMmI5q+0HTbZdWd8A+sAA+qPzAfQ3XX1dWgTzmOt6DWQY5AtppvysYm1wSdD343Otvn/x+wAqOqNoMl02swglbMSAPBDC62qnDH7gm3zuZViKlcuyB8lfpWpWahnTx9Nfp9FeGg9ldThg8QW/t7PRkKOLCyye4v1iHwHEYF1Qe8H2PhLgu56ebabVz4nXnTghXRRjMDAEwAal6VikJgU5CDBSKirVnQAds44luXGlKdcuN1KhrkKxKoVSTSKBmCcU7zG7XKTH+IXtvkDvvVuYUPMAAB9A8omtEUF0rSRMJrvtmhSLJVkPGzKKSnKmF8sgTKRehtBWLP24QP2Rk71Qzoxi2YwK0lNrbia8LL6BVcVAqnRtemA7QPd5Wcxe1xWgIyqhoPzkQ/MpkStAVxV3nBd5ZhxPXC+ivnAW3W3XnOruAD4nNIfnJNuJcf1ywJQysci+AitEhRwBqJVlYYyQrLFrdV//CwdsOhqj9OaYowZgxMH/GTZ42BMAKBvjgC4b4wix2gnOGNMI5dTz84kTYgb9a5d6sjoay/1rlzj/vjWZck3p2B2un86E++S9iyLqHlUzMdXHwLmiFH/kMlqNKbLbfVWrRdeBA90OAfrEziA8VgG6FlYwP42f62PtnG+MoXCOPYdrFNO9A+wRB+joWuLfHahj/VH93PUvOB6kgJQAHTEIYwWVCgH6j39mtuuWP2P/93e9nwAdV4YsHIjwQt9lGsrTW9tsplwajthVtSoNwbnmbTrTraVcAL5IknDKfQ+rRQfomuvkAHpitGyIp1Zh31eRa3fslHKxLI9u96Q3jaQOijfOrjCQQhyS2g5AzWeyYoXnNmkpy9lfNj6InGC505Vj+/ZJu1aTlVaXXW8V7U2kNx7LR/72Y/MB9BvAtNITAs8HVtMte/bJSrMl6P5jDAlFV64jCnkmFPxDwWZjNJaN8YgFkKe3tlhwwLUHPX63i4HWG7K72ZRbdq/wPmMf4csBuo9jsDaUG3n0UUasJTYwTAgR5zVeA2B8AD9zG40CIMe+XBuP5OwA+zKUfjDhWaVFZVXWZzHQ80PTpvgPJ5+a6dr9Y2CYf0qveQWgv4J3e2trS371V39Vfud3fkduvvlm2qN913d918yvCBX4Rx55hL934sSJb6r+/v/3Ap543f/e90m33uDhloldKJhGAPCNj+CDDjUCwLlRKKvwOh9F/EawhF8quuZhhETeBDIw01eqcu6ywO48KF0A5jgspgz6UTzQjnsqy1LnbFml0qaNCkA5koNxuC1RPJHX7tojRxaX5Jodu+Xg4g55YnsiT25P5Hx/LKd7QxPCGhgwjqRUrkq93pF6rSY7FhaY3AnsWeJYauGI3rCf+cp84j8f/I4TnFViImCWH05P8q6FElOc0u6q0DmKe05EzlX0M0CuR18eoGtTwg5Ow9zqy6wJLOfIjd6tXRlnLyhIdwVh5iY2q0RPbCpEI/FSqzVXQW/QLxt+1yqiAzVvPB5V/iN/NltHKZ88PPij76KVjxYc9EsF9RSM0y7Efjpgdw6A+3VizWEeHXR3UOEB0s9PAtkIInlmOFGgb3P43rnyjqlS+OE/r4AJ4JvJE0TVqJGAMk4s++pVWaiU5OpWQ3ZDyKtalkV4NVsXCZTZhKpN9in484XpRfZn/N9Lobi/EKDn2QGgpY+CsTyz+oys99dTobxWvSnNakMWWl1ZQDe93pJuoy3lJJJKDHu2nmz1VzlnS00I0NlNORwUOYqpcaSkJIsN7YZAKKhYiGWhsC2Q1HJ2zZkBPLsjUihRMDm+c49c1V2SarVJ9e6g2JJpCSwZ69Fw77h6u95hZ4q8EKD3DaB/dk6A/tYjx9X32uzUSF+0ziXnLHPde+9QsisRQnRKxZJ87Sm+0/lzdN8zfQO3KbQOOq2qtIvHxDcdsUG3e8zEHTZu2MOdSp3P5UUl7vt03lOvC5I3iCqhO6I5FZwKVH3a97cmb9oF005QNnv71fOz+dv6OXHiVTcSOKdgQkQW41hABD5YKcoCNtMUrepYrurAm7okr9rZkMOLKAiVOWY0LVYkKNVkOIllaxTKYDyVje2RTMCOCMbSqRTkSBdxCNZpUKuP5O/XBmTKQDEchaVavUkrtOeCqvSjopwdwhIL/sTodon0QV+OE7kUApgXyKiZADQY/RmylPjmOIgDdFwrA+hK39ZY2mnovDGeF/P1D//9fAD97Te+lZRsAnErEOYDn5ZzNTZ7PPSOujbKfc7cS3+X/zlzs9IC7gvVL5Ru7voQ2U+Nr1YYTX/qmqMgrO1F77BTbC1WdwjocWAefbmKWdqiLFYh2JYwFuN+nRmgw5zIiIybSE5fmA1o+rq7+x1vlkc2erIxmdJqDaMS5wdD7RZSXRxAKROuxHv22V2nW7sGDGGkCbrqeA7s43Teul6EcGCJwIlsE6Nxu/uHU6h93ASFMPw/zkwysExYDwwz7GGMhOEcpvq52bshDvALINcsQMlO42iFquzzPkmRZ+O1Cwvy0/fMT3H/6R94d2rvh3WE9/vY5rYK7m1vUxAOYmxwtSlGWohGnkb3D5ucctCs1HcU5uDgkH4MjZn5by6mdH8AAAAgAElEQVRY1eaoVVU88/DiMplGp3pDFtPBfMT+CkZ9icJARuMRXX+0meHihnoOI3pp/pOJHmJYBnt69+KivPrgIWlU67LYVOaDivYmsj4ayn+8cz7x33ddsyedC3eHm93tBXb+YeeK88+LX8pgKwrs18BmYnEojuWsddAxo45xJLWsrdClA9102LPdsHPZ3DvqqY2rr1nE+c1xwG45RoTwvBRxpBaMaSiZX72zvHxPeDOMRRZzL8De1SKW2vHi/WiRKKfkZzEIZ/9HHnv2CkCfGaG9cr9wBaC/Qtf2N37jNwjI9+zZQ5D+jne8Y+ZXWl1dlXe/+91y1113yeLiIn9/c3NT3vKWt8if/MmfyK5du2Z+Tj8AP/2u91CcCDvfATor6wA+04BVQdC5cBiuY9ZmqvT2tUkoq4HI6hQUQfVLZbc7mvB5cDhDEKhcbmiiyMJsDqDTqxmzW30eSdo3BaBSJd1KpSklAnTtoI+mAOhjuWHnbjnUXZQTK/vk6NIuOdWbytP9UJ7rjeTprYEC9KhPxVb8VylXpdHo0m5k50JXSkimAdCTRBrhSMJpIH9933yzSr/9HSfSbrJ22HIU9zS51qqwgnhNjvJddE2C7HcT88nM+abrQWD0+cs66OZ7ThE/TdhcCA2Hn4sHEQYZHd4TF1cuToXhTOQKSUUDAJ2icCqMA2Eg/IToDTr0AAJILtClOvCnsyWseYD+wHv/FaldWHd4vTxAd9sfHBT5eSy1rsn603gPQ6iiw/onCOVSMJVnhxCWC+XxPjpv2sXR+VLcdxQgICiIuTKlhkLtHQfWAjvn6A6oCE4lgeZzLEfbTdlVr8ixdkv2NursFEIwSe8K/J0hXGWEPFWWy8CnlR7yxHckzxCJm3cG/R8AdI4g6CG7MdiQ4WQkTz3/lKz11rTYUEikDuBXqcmO9oIs4bvVlZ3tRSknU6nEgfRGW3Jh84JW+62LjH2PLtPF4ZifqYbnKFdld1c9zSkeKLEsyrqUE3TNVXX8zFAB+jBUPYBXr+yRqxcX2TnFbHBQ6kpQbru7s6mmf2sA+tuP3ZCCcO9icj3Z3F2q8WBdbjxGZ9BVAIidMgPJLgKHpJCg2oCBi8bh78FgwBdpiNYdUvVsfR5SY1PrNuFcJuYa8U2WSpqYaiWOnRGbsUWxQJXkdf/7eAt+ph1C+xxYp9pBi+WR1TMzJV5+Tlx33U2kmOOLGhWSyHISKUAviSxgxAZq1PBJ78K1oyjXLDfl4CI63jUpFGsyLlZlXKzJ9gj+u2N23C9t9JiEjmPMRhbl+uUKATq6tPC9fmANPr0R7dPA6ShUWxIWyvLspCLbUUHOTwoyJEDXEQtomaAjPk7KpLpjdhXXSAF6LCgJphR3nHmkY5tIHMaVcJ3ROSsrQFdvdgD0SL721flGob73xreZSBxCg0E9A+PZtKkznBT9aKw20J7roBN+u7ClPUYBetbL1KdWyrzVhfRfbYTNCz8AqT7aRicSnvvuLuJ6CzoeoLPrqkOBwrqEkdTAnCkndHjY28JMuMZCjLg83RuTAdGbqMDe6QuzUbV93X3mHbfJoxs92ZqEZDvgG91JzPy6XzfWDwCIF7LdZjQD6JkCue4F1S6BlRi0TbQGpnoUy3VwQgDQAQMBvHX0RSM+/MJV4RyxDq8HGjvAOUVwS0XaBLJAznvoYl76vlEQyDNvODtsZyzPaO5pzGqjWFeR65YW5efunV8k7se+711ShdWnMeiw/0+ubwnst57v9chcKUdTKcaRlOynFl9iKVhOkRYtsX8gZof9Y9RrsBFIFPTOv2WhBNModsOis1yWg90FAvTTA10T3UaDMW402JbpdCzTAA4jyAfzQByCrBlAVx6Elp5AtIdn/N7FJXntocPM7ToA6HSagVhaLOvDgfzep+azWfsXR3alAN1Zhu1aSyq0PatyLMLnBdFdR+f+UKdL8TgAXqyX84OBnOn1CNSf2NjwHck1BmtMzLO/anmJ7KudxsSj2r9pnGAvYr5du95aDNLpPGVWkdJuAB0ib/Spt3NKqSIoPGmOg1yHaxLCmHZfXZA3gS5HOm2v6Qvyrj964rmZzomZAciVX5jpClwB6DNdrhf/YFTMG42GvPWtbyU4/EZfH/vYx77hv91xxx1y6tQp+chHPkLvdHw9/PDD8r73vU+OHTsmf/zHf/zi35A90g/Af3fb90gdVGNR6yz1NNaKXRoAQqXvoGqMuRfMlyIJGsUlGSXwGQdlEwrqoNcA1ENcKZJSqSa1akcTOksOQAUD7QzdcBz0QdhnB12hLbqo6mldLddJdccBhqAxCLZkGo1lpdmWhVpDrl7cJfu7S9KfFqQfFmVjEsqlEQ5PvD66p4lMCwnnbmtVeOmWpFuHuJwplePwNQGbux741EzBKBVh+fbjKUBPLeqcksSE0EJfOkeunWGdK7VEyIA9DjtXfPfE3IV2HKRnAMK7Y3pgOUBXurPOTblSuwIHven+77SOse6fd88v76DD9xX0Z1C/dd6wiwQEPpr1KivVoySRlT+c7brhPfi1e+L9d0jbRNXs7ZkoVpZqaiHDbeqyLpMf1X3YSEWJXJqGcnYcyIXxRB7vDWQTfx6pl6gXIVSkUOcN1eddOx8AsegKQrMXu7MFcFAQOdSqU8Dm6nZDdtTQRS9LC8UKU3T3bhdt3hyYkzqL7pw7EFuSbaR3Ly2AovlSALrPoL+Q4g71dXQinlo9JWu9S9RYKHAuHb4KANk1Um13tNqyq9OhiGOjgLEVjKaMzP9cO8UYI6A/OmyxynVZ6e7i7zMRAkXT6a7TDXqrn99clX4womosaZ3WnbtusSv7Ww2p1Trsok8qSzIuLyn4SEURbc4wZ0WY5/YhfoTRVLyDfvecHfTvP3aDBDbXnWeSYF0ivmG9UcndwZGJ6IDOiEQJAB2xDaI6zJWMcpwJLaoKvFuMoaPChDInVMXVbUU3paNnNj34PbU71P2ZgTcNJSjEIMHKxwMfC0FsYJz1zW54D8+F56HdVBTLg3N20K+/9oY0MUWBC4nrjngqzSSSXXEgbYlkR7Ek7UJRdjSL0q0XZf9iU3Z361KstKRYacoQ8+ZSkbXeUJ5b35JiMJXyeEIwNEoC6daKcs0S6P4Q9Cxx/vd0HyJkiZweRNIPYbupnfHT07IM4qKMBJ7lKJKoE4HPbwKcA+hwPAtXjywG8mls3lfZDyyqklGhoyJ4LO4XjupWTTtynUqRzK97H5yNMZSKxN3wVqmXUIy03ra2xdPE2DuQnig7wOHjDYy7BZrHddxXXS/6OYymlorFceSpCHCm54p+aaHI6c4YD9KzGow3fG63SrSCkr1PsI4AfLQTbN11dF1NY0aNLp2F4HugwnMOvvWTKJAvn5pNr8Sv3d/9wG3y4MUN0ufByME7A7NP173O0qOIhv0LoI51DlDNQpV1M/H3EHtTVpaCG3wOFBz6sAekQr5ZkxbLfBxGqPCFeV2sKwBmXA7MGOP3vFhXKavIJIoUAF9L1bK0YQvIMQAH6hk7gvubBeJcYc+YO3Cfwb3CKALm4SES94EvfHWm3CR/xv7w23+I9w2fdxv6EJiZn4B5EMvWcKg2b5ORxFEghTCgSKMr4acWo1ac4DpkMRH7TC0zwUQEWC1hzI/ePTaixm4tADbVfgjOcY0x2oICFSjpaCIM+grQQ2gG2ey0CoNqx4JFDrvnPmqgDAPVEdq9gA761VIt16RabfE+YE8wnk8D+YPPzGez9g4AdPd4dwZTERYRWOX6WV1YEV1xgPTX7FqR63fskFa5ytE9zPejsfXoxrrce/48182FwVCtaiGOWS5TOwSsDXTU0TjY0dBOOgoMuFcA9zgj8FyIkQDYBPBmd8zClFnh4Xc8pugYhYrdev7sv6ujfWlAQIchjUMeJbCHPvns6szrbmYQcuUXXvQVuALQX/Slmu2B73//+1+UGvvv//7vf8MnXlhYkDvvvFNe97rXXfaYL33pS3L77bezmz7rlx+A/9Mt3067GxxKStvS+Sz8RKKNwwhzvxRWYUCIJQblj161ZYkLUOLWwKUUd1TzMA8TSrlcl3ptgYlj2QA6Ag0SpTCGP28oQaAAHQqnCBsQ+kIIqcAzt4hDr8LfH0y3ZRKNpFWukS64r7MkK60FkxmpMMnoTdViBV6lYUGtOTD7hO5fBQlXRQ9MADCCsyKK1qF8/u8/N1Mw8mv3f7z+OGcKOTdtQNK74dlBo5NxDraVVmh2TalAViZixE6Gdd8dkGcUYOuSGA2MIjn4LDaDrrPoCtCVwq4UWBWKcyCvHVeng+HAIN0OnTtS9VQ8DACdnXMmEkVZrOEgKslCo8rPPIxFlj9y50zXLZ88PPNj71GKe+6sIA2LHH5NYJ2aqfY4Wb3D0kHZjuCLHstqMJXTo4mcH0/kka2e9MOYIxj4cpE7UA9RRe5UQFvNkicAbqqYQ5MgEemandDxxbasQHm11eA8ITtvTJhVOCkP0JUui9NTD28Adp/ew3tweqT+hP1J8PIAdIw0GFDEM6P4BW2HUxefJkCPgz71GCDohrnTgqmUL7eastJp0+e8XdKkypNZdLs4zjIJKOrVaC5Js9aUq5avIkCv15pMZtGNo1L8ZEuicCTPrj0nvTHEfoYE/JNIS25HO3XZ06hKs7kojXpXJuVlGVeWL/N1zdTbcxR3E0/S+WmdQX+pAP2fXXMDYxsBmWEkPj+Fs0LuOyT32D+etVDcibOGiCugG4YyApXbqkosgLnzAZOiAgtZ2E9LtbqKjFkH3VWyXTPAKboASdjrWlhzcTAvWylsw7ZA8undegVeSqfETyRrute9SKcdVArfUahOY/dXzj49055NKe7XnGB3FLcFnxfrbqcB9B3RSJpxKHvLFemWUAgtSLNSlJWFhix36lKqdaRYbcsgKct2UpHV7Z48e3FdGlEki+GUgHgYT6VTK8pRAPRKUZYaFcbUS2MUihN5shdRjPS+9VDWpyJnw5IM4SBA1faSVAsls5XUgIL4j9zTZ2l9ft/vO2IbriWXgQUdxEkF6HBAEGlU1TIRhbokDuWuB+c7J9524q1M4rUo5F/6fz4a5f1vBeuGyvMAXZnu2hWjp7TOxuIntQ+QeFM3A0J+yhJolAAqtXiuoF9fhWshSWRtFBCsQTMCa9ALxOrxbTaNiJVlpSrjd2g1SN9pHexHkR1n+jCYpCMf6AzvbTX5WpsQdQunctfj8yng/80/e5N8+cI62XvIDZQRlYmj4iqimOh7E8AGc7UAPBy7YUFN963aq+le0z2D3CbkSAlEy9hdZ/c8ka3plGcObDdxbsCaCnsLBUjkQehYEhThrMW8tXXQ4euNkahuuSTtcgaSFJTrfaOrBsY1CGxV24I0fBRJ0BiBkFq5IkcgEvf52WwR82fsHW/7l+zuIkZcHAz4nqFNwdcYwxpxKsF4SBahhGPaHLqXvHbFNYYwrrjeDQQUfUywXOVZgM6y+rfrWeI5XiGEYDCKmyoA3OTseUkBerEow+G2TAMA9IA6BU7NNsEIQmGcyfhtLbzZqHyhyDGAlYVFufHAIamUqiwAEqBTFFbPwv/ns/MxI3/wyArvjzMX8bpgiTKOmm2uWxPiPmFvvGHvfrl5ZUW61TrnzbHe0NH+2tqa3HXmDLvhT21t8nmR9+i4YJVrAT8B9NGBxxrBOqXWU69HxsYWXDMgSGoEPY6f5IpN3geiHg6tZbH+XewW91Atku101aPNEy0H6PYkzMHjWP727KWZzolZ8ceVx892Ba4A9Nmu17f00Z1OR+6++27Or+e/HnjgAbntttvYlZz1yxOv9x4/wSooDi1QvjzZY1LKgxygV7s8bg8DcWJ4yOq0cllGIRQjkQSGPIyxwRGgKApUaZnfmIKvbMYGAB3q7CNSD42HDUkqBuJiAcrYEEFqSKlYkUk0kDCBBYeJtVSb0qzUUqAUWScfhQJQIGnrwW4YPIoBytARDqlCC6sgvJMpKPZxKI+dmk846Tdef70CdFr12Jyf/dQ58Wz+2+l3+RlWvWeWqBGY67yVq+n772T31tCszbV7wpc2zVyZ3DpcTt+9vLPu1nkKzBnU2Y3RZKJGESWhKjO6zJjNRpV3CQC9UpZuE4WTovTiSJZ+f36AvvoTP8KuhB8uDkN4cBj6dZALeiDAHjoLGJ2ATzo80QHIT4/GcjEI5cw4oP3axUCTTxyoAEvoegOkL5R1thV/RlpOMTwkDeyYF2R3tSLNUlH2N2rSKZdkb7PGrggo/pwzTAsdCk58Rs/vDTvpSCnQ+TQ7GRcKVJFFPfhwt8EuOfDv/9NMB6DvV1DcvYPudnooMKRCgEki5zfOytZwQwbDdRmPe6SaY00huZjGBalXy9KsVqi8vqumiTdsgyguFiExr7AjUa00ZImd87osNEBTRMcEG1+VyrnGY+1+XNw6L6MxbNouyiiAj7fObe9fWJKdrbYstndKt7kkYbFBJff0fhuDJKXi5hXcUyBdlCgKpMcZ9LF8bs4O+ncfvs6EpHRGUktn2tFGVxyJq+s4eKxywIyEjGJY5hurSZSJNqauFzpLDnAAkI65RO+u4/mYuDp9Pme9CHEfxFjvhrOAlgP6PhLDopuBcCT83tzxziA+C55DgZwW6fA8mtgquJoXoB+/9gbtk3Ivqt/zddWC7CiJ3FBLZAVq/xCbAnBBsRZpfb0iRSih11tSqLWkFxZlMyzIYDiUre1t2VEQubpc4HvGbHGtUpDlZkla1ZIcWGjwWgIAgcL+4EYkF8ax3Hl+QseQjaREmmsBDgpISosV+kj7hAmYEgAMhBkGyHCdPE2l3gY7TQVpmm4H9r8yI1DsSKRQihgzlmtlUtz/8L6759qzt133ZlpEZdFeC6eZVoX3tkEX1vcI0Kf3Wy2kvOAKsEi2gHVgNSEHKwhz81Z4tjWGuWp8ofPsWijaQVeAetEA+uZEbS0zgK7XTRN+LTh5YUi53hkDjFapYUTBMdorRiHBB84LMrvwelEof/PIfNfuk9/7Rrn77KqskWmhIA1nrjLF9H77CB72JgppuB7o+qtAl/ueZ8DciySu6ZBa7OUKWgD1ehRp0cvHVVC4xD5moQ9dfKN361kLEVWlubsPuooSorgGQK7sGhQw8qMxiN9kwhn4gwK++qB3XxJAf8/b3kmgj/NnfYBCbUiRNtD7YZOIolUSoXOO2fOQDjsYu8Hj3EqPrB0AYuQlNnI2DWPmVK2aWdPVoRskMqWgsBYLuS4mQwJlrA9kdRhZxJq6qtul2F5TQtqXjgMUC0L6oUNkDTlkEKLfrucazv4QxSUr0CDvW2q2KRR83d79Ui5X1F4R8YnuLQhDofzBp+abQX/vdQD9WjzxtaIO5/ZNtqMyIBFzsM6vXdohV3cXCLTxjcIP1uOprS15YHWV46KwMsM6wVpImTu0cdM8FeuW793GkbCumTNYI6ZV0WIRy3o2Fpk6nuTGYlgMcuYDg8nlHB1nZmkaaZ/QUksfdblndXOmWDcr/rjy+NmuwBWAPtv1+pY+GnPr6JKDyr5v3z6+9tmzZ2nRtrS0JB//+Mdnfj+e8L/j2FGKXHnnlgARwJUdcwXsAEgIsExeMadcQVVddaBLhbL0JolsTWLSyLJKNYJ8hQk5K8SQfbYvJgmJqbmH0Ev16j5CD8SAmMay99isdKRSrMo0GUsER0x24nFI47VxQNN0g2Aes+VSrEpSwoEB8S4Vb6KvLXvt6rHZIDUO3rjqn3r27Hxzmb/++ldlFPe0g67hz6mJ2kl3ZXYVidPOeF4USGmI+q00Qq/y5/to2QXM05S8OqqpJ8VmcnNG3jln18XodS5olafbOSUK7AIA1pZZqy2UIZ4DEZQq59m6rboUSwXOfS/+p7+dOYj7utv8yfdaBz1/DNop4evEOtEQesIa3J7G7C6cHwX8fnowlCf6AwL056DsC3ogr6sVHYoFzozX0QEpq20gADnnsoxCB19feJ9fDe/mSokq7Quw5IP/MosvOmvoVnVIrEhBTkcH9PDTrr6WuLHulM+qxRskKWSmhOjqFwhcD//eH8x07fIA/RGzWYMVFA5vqOqSOYHCgIhc6l2Q/nhLtrYvymC0RWCu1Ekk07o+ShB4qlVkbwN7K5YhO8h0zpJaBZ3PFWnUmrKyuIcdCsyxk6ZoIMfXJSFuEsnW9gWZBANZ2zgnw3GPKsdIUvYsrshia1F2dldksb2D0A7fLNRZZzgTVPQkyKpcRkn2DvpLBehvPHhUqgaUPKF2UEyKO7sUqrKt7wkxTzvqXljD7Ot2oBZLiHW+YjXx0s4YClroYMBOx0dI8Dj8Dqmb1ikn1hHhtULsRJEgpdmTNquiQpndmsYFtUTUO8C5Xxub0dihRaC8bZfHjZcK0AFbeR7EIQuer2lWZG+lKG/slOSqakG2+gMZjSfSCwsyigoyLJdkhGQUGie1pvSmIpvA8JOxBIO+7KkU5Xi9RFYtWFdYk/VqQdq1shxZVvpnu1YiW+eL67GcG8XyF2fGBOoDdAYBqCrwLC9Ks1STCgtjuvUwgoXrpgBdWUnYAyxWgLpcQCyAOFhBFvC6xaIslrX7VoZfNc6IQsCEeGetTGbK79w7H8X9jcfeJMWSCks5zz0dfrH76HvBEQH2KEg5KCeqP7uB7xLEpgw4m2o1OngsDFW0+Aj2Ews8xgSBaJ6PTeEtuMo5OugQ7doYw0pQbRExA+0JPIAEGARY1+oQkhV8cCVdCX40jWR9NGbXD4rgeBzGAnQ8APc3kr88+Zm54t3Hbr9VPn3meaq4O0BHXOYesL0KIINuJfYmYiz3IQG6FiJ87lsBnsbyVEDVrUrts3FcwIUq3WmCBRzELCHQAlD1vcqxgJyYHu4Drz3n0RE7lGYMcI77hkJHq6zFi3REwZgR0PKmRkWibikK0OenuP/I295BZXrEsY3hiDEGVng4f8gsQSyxmXPMnaNRAjYCgKWLKLLjj/EGjvnoqMBwiiJISZbqYLtUZGezRboeoiFHC9HQiSPpDYdazDDtDmQniI/XLC+TXbRSLXIPDuAlP53KE+vrcr7fkwHEAKdByvxTgG7dc0momXRgYVl2tDtyZGUPc0EAdDaZzLceI1G/f+d8Ku4/fuKY2o/l2GlpZpKy+zQP9VLbvnaXKu84X/CN9Yi9AL2ExzY2GPthQ0ztkqIW63sB5u59z2W2oy5EyEKJQF9D2X+LdTx3gbR/4zekDMR85uQsIU4KmPCeDZFpYurHKxe6nSO0+NX8G7nK/WvbM+3XmQHIlV+Y6QpcAegzXa5v7YPPnDkjP/iDPygnT56UAwcO8MXxd7Bo+8QnPiFXXXXVzG/IE/63HT6kqpQ2IMfEuaBkJXQ1ECxweAMgkUoNq60KKsoItqDRlKQfJLI9ASU+oSetdslx4BckkLLOWQYKTiiwYZCGKbpR5dQuRT1n+fsMHCgGdKRcrEqxlEihqB0iBOK0A0EbNlBOzUcW1PgKlFhxiCudmVXHJOKsLHyfOyU9oPoA6FEkz5y7MFMwSuf3AdAJ4rTKq4lARsXWD6qhkwlSbhZdqWP65XRVBVI6D+hMA++a+WGhM6weZK0jZOJxHoRV7VOrqNpp0HlrnW/VOVUF6VkXHQCdiQSAxmUAHQlhSZYaValitq4F0Sf4EIey/BI66ATo9InOjpb0irDrh1lTdHYTOTeeyvo0lOdHgVycTOW80dkhDHchCKjUDLE49hdtZhDJKRgAS+ic454XE9qK1QoRP9+uCjrmJTnYbtJmBwJw+DOoifg9isiZxWBGddN5T72OmqyqFZlSVSEYp7N4WiRAhq3z3AB06lGN+wlA8ro/+tO51hxF4gZVWhsWkzHX/mjc42iJUwPHwUCm04kMJz3BXDrXJTUmUCRIpFYukkYMKmi7Cqm3kkyh91CuSbPe0Y55c5mdiUYNtofYS19fP0PLE4kEARwUprK2cVaGo55sjgf0Tt+9vE+W2jukVe9Io9pM94rfdRbvvIueWsqk7QHbH5gndYr7/B301+67mnRE74rr9nQ4kqcfZ84JOgKioB37ld6yUJCeohsFFpCBZgPoDtR91pA2bGXQm7VTrMXITHQKf+OaH570O9UdybyrEiuw0oJp/itPmXaArpRZZaJk3Ve8fiz3n5tXxf0mG+EA6AlZYrm+VpRd5YK8ebEsh+pFiYKJxGEk50YFWQ8K8nQQyVnYb5aq/B4DuE8L0oZtoUxlT0Xk2romhpg3Z7GphI5tUXa2VSsCNdd+JHLvZiIXJ4ncd6kgW2BrAfijw8yxVoAf0GaRqAOwF1RrgFRtvccgO+Dbx2UI0DHSAzDJwl1RdlTK7Ji3UXyGfkkxYkIMgI5z4pfvnk1zw8+JW65+I+d19c5pO8zXoLoI6B1VGrrFboo66b6jawJHlHRkp15SdhBinArJGSgsa4cNDCDEfIAMrAPMWZO7YSKkIWed4Zus3VSo5ZPBYR1crlITH3QWVk7aIO3oabdNO9QA6ejIApix++42lgBXUSh3PjofQP/47W+QO8+cl4sjdOiz9+VCp7ic6NA6fR17wIuozqZwroLvHBd4ZCHLeQw2fkBmjVP57XTiurT4p11kZRViP2kTQ33V0xGtVO9Fz13VDXArNe2YsnFgeheuBI81ifeGkUGIz123uCQ/96WHZzonsI583f3cD7yLAB2pFRgguhZ0bBHgUK3qNBZBcA+AcCuAfSnGTpQVyPcpBT4WauK4l5NgKjBvayWh/jtGLLBXbdBZ9TNQVPcutP7ZtR7gAY7O/h6w1MoljhlGUSzPbG6Sio+9xnXkxWAwJsqqKQCgS4DeXZRatSatZpvnLiwbdSxThTcxcvHl++cTdbzj2sMsSOHC+HgaLqICX89SdNPSbi1ObK68agXdEq8lCrg4IyDK5/td14qKjsIyEPtroarFb41VqlmCswLjFPh/vBew5fa3GxSV05Gc9LA0TSNzP/GxSSe0p3+23/ERGtsM7rWusV8sBYwAACAASURBVCFz+7hvdWPmdTczCLnyCy/6ClwB6C/6Uv3jPBBJBubQH330Ub4BiMVBeG7eLw/itx3aL2VYoaGqnA4aAUxrVw2VPlRMEaBZ0S+ItCvobAP4aJV8ECTsog9hbTNxyKUVeXhSI/giqBNsUrjGuUMIMk4TtRkjplU4UPR56mUF6FXYe5V0PsdnObWbHlDp3e1xMPdeqTbZXUfCxllzBr9QwhAAPZalslpO9COl4z91bm2mYOTX7re+Pd9B90q64SQD555PO0B3OKCJlQN0pbezy8M5OQfoPndo5QhLHrKZXQXrWtIwexxanCjY8Gp+BtAVlLODmrNY0yq5AnR0kiBtpR10UMPVfgZezVVQvptQMS1QiG3nH/zdTNctnzxs/OR72V3x1JTwmsIs7rxakNVpRNseKLJDBO6Z/kjOAZyPJvL8eCIDiMTFmBeFnR8EBdH1RpKt4nZQpF9G5xx+3QXMmMdSScK0Y96tVOT44gKV6XfWa6qEbSI16Jznrym7bxQRQ3dGO5ikL1uSp505nfel/i8pxkUe0qujEX/iQNbOmcj3/NnHZ7p2l3XQCdAxTNiTOJ6QVk4gjhlCWqvoPN8UlmnYG2SyKTjHd6tSoggX9giSKzBdklJTmvWW7FpY4ax5t7FoRQZdpG735ernvsuVDGxk8STWDvqoJ9ujbZlMJ7J7xwFZ6uxiFx5xBtdVBW2cLpj9JH5NXwv0efWyR5rEGfThS6O4n1g5QGq/d8+9g43XdHE4Hytx4I2/d5COfYJYCNVc/MRsoNPLkYB5R0zHRnR2F7+L9eVJaz5e+2t5ykegDoBhxUZP4P13aCll8c/n6Ak+bC1yPMlm7PE43VO64gB2EVsePD+bfY6vuxuO30zgo0mc0k+PVYUU9+9ZKsvV9aJUqeOQyJP9gjw/EvlqP5DHBqH0pcTvMCxIGBZld0Xk6mpBdlcSuaauIBpjGDwWCNJFmrBXKCQSFGPZjkS+uAkxyII81a/KMCpKUsIoCQC6UkDB8EAnrVwpUbSKSS78y3nEAEQ6QFc1fIChFkZ6RKRdSKRVRNGurDGjqmybWjlhsQ5jIGAN/OynZxvp8Wt344HvoIOB7kONccqiUMBt4viO3Xm/OHJEKjvOPe/sY50KtSMUoGvXTS2YdP2hc7tUUyoxzkvAZXRNVShOQQAZWua8gjUIZo1brrmlmhcTWFDM2a55gQn4xS3aWHimJqqf58oa4jmF4n4Yyl2Pz+b44dfu47d/BwH66nDC+8ZY40DW6OUvHAXz/YP3mAo4qgS27oncuFIK0H2TGaDROercmrRiiJ60VlQzbQqO9JkntTI18kVnfVUdczIWoVHn/Qx2dgLOW412RVmoVuXE8rL84lcen+mcyJ+xv/wvf1g6lRrPKMzS4+cIwrxxzJEBMJyswsU/A7RvgCEEW0kT0UO3Fu9JhfGgcRLKFCyJKJBqMDBKuwoEgmHHdUgmSlEK1QaZjaDD49+pXwBBVmN+7G83TXxPkSms3y4NhwpArciINVSulKVSLTO24RxdBEDvLJCpNsU3gTncAnQEidbAk5GsPn7fTNcuZZUePcj958UvzVdtZDEt5GjRlrkx1O1ZdDNmR6FEmj+6+VgPtNJEMbyCUc2QBXt02DGXDiHHPa22zbxbMdF+Z3UIPZeI+xjnyJGFtnRq0O3JALrOomcskez8yoqRKaXduTHpGJkl4hnWT8+zLz4/W048Lw658nsv7gpcAegv7jp9Sx/1qU99Sn76p39a7r33Xul2u5e9NrzVb731VvnQhz40l6e6B6PX7YfnoynMsmiIsqnqcbJiCoAeqToqq8HmUQvKFpIFVOyDsMDuCL77Aah8eoy5EAsBOlVRUQRQRU+vyHviDwBhvWZNZCxhq5Yg/lFhkEYnQRMv8wJnHIfyKOj0+prVak0atQYDHgC6djh1Dm4wGZJg2y1BGEYk4MxmLKeen61a6Nfu/3zD9UZxx2fVCmTahXAakXfOjW7kwMQ7X9YDV4BuAjYIuOnjclXcFNBfBmRsfpEHmh6AmsBktmuYLc/U3Q2cG4i4TMUd9FyAdaiZQ8SmUBBQwHGvqYAP2l5D1UzhO37gj2briuSTh82f/GEeWPhSv3IR+JojUV8PQq65M6OAnfNnhoFcmMDebyqXJqH0wki2IfqDCjZn27RYxEOQCr4FJtyg66M7hkR2uaxdsr31Cqv2V7da0iqVZFddvaeR2Gq13A8//emzm0qVdIV8PTBpWWLDGC6q4yCdO6lQVJtCzBhHoA+qlQw6V2/+6H+ZK3nwDjoEEZNoQNeE9d6ajKdjmUxHBORRCDcFOCSAjqmdE7x/pz13a3VZatTpbtCqd6VYqkqp0qIabrPepttEtQTf5qw/64A8A+gajpRE7AX9REYQ/plOZDydUN+h3VxUYTmOo2hHj6s0vx+cemytO1/9CtA1paaK+3BdgulIPv/obKMVvl+vX7kq7aArC0dY0AF4pZ1SUe+XC2HhfThIdpBO+znvjgQqcIa/04KYJnVqZWP6DkVNtlMxL2famPUZ1oNfU9wr3Cd/TQcXyjC4PAlLHR4MdvisOTtVZgP1QoCODudX5uygv/r4q7n/fX+gw7yH+yuRb2sXZB8Adx0K1kX56mYkp/qxfK03lScGIQeLJgmozgUWXg83inJzpyT7ayI3tkD9h+evjoEggaWeQxEz5iIXQyFA/9pA1dvXphXqKACgM7004STOoUJUtKoAHSAfZ5Xahhk9liBdC6CAywDn6KDD9A8/4eUOb+8VqreLNMsKoBeqUJQP5Rfvnq0gmQL0q95A73YXkMSi9jWiNo96HrpmAH5mAF3HytjRt+I4lMIherlYrbD4gNjC0bQYs8FFWa7BmwL+8NrhBesIcZUaGaaFkdmrOQ3cC2U2X5vuaWeGqRo+x32MAeDnHXG5zbKaqkNKocaBiL37mcfn62b+xffeKp89d5EAMjE5egfJyvMzEJ2OdWSg3GnsmfCiAhKsHAqsWuxWccUsvdJrpHoRWgDL9rY/SosWqviPcxdjQjrzntm9Gfzhc1DA0+aJndXi78WLbNq91yIK9FmOdRfkZ++bv4P+a+96H3MgPCFeG3tBwXcka5OA4r+hCa7Bbg7+9X249TDfM0FA2uqpLpCOeqm9ZjmJpBoHyk5xWzUyhbRgiP9qEG9DMYrFaitmg7mCkYwUyCN/VAG/rdGItHAUyfE6AKf0o9fkhY9BrOU5X64xTgCg+3w89wDPO/zuVPqnHpjrjP3h63MUd1svqTZEyoPRbAEMFN7XnKCej4Ll3XpUC6nI9wdw7kKsiPXwRFfWhxfDtYuOznt+ZGlfu84Ouqu4ex3IG1bZOIfnhNo0wP33klEK1rO/0HM8x/TEa37mufMzXbtvKTD6J/hiVwD6f4c3HbR2eJ3//M///Nd9dx/84Afl05/+9EuaQb9pzy4pIIiTfk4vDZGCAnRUTBGoMXuEn6D9supfUp/aMmjDTIKg4A5aD7rpSuXTuayYnS8KhkD5mAehBmtaNaXGL37QGo1IC5bMGMolqKeWpVSuSKEEgK7BRyfUtdWO7jgPbXg+VyvShq8khM9AS+NBimpmKBuDPmfR20Xt5sNLB/H/6RkFMTzx+tCtKhKH2OY2ay4W52ruGbU960I6+PbA6MUMVkJz9mueTuRxklbYLTl1arABSVWH1wtHgM65QU3ulCKpf3YA4Kqf3iUEyPUOehMHLBJwo1c1mfgWpFxDx1VkMwzl6J/Mpsx7OUB/DwE63i1sknD91gIcXrE8MxhRCO7JwUguTgI5Ow5lbRrRsxbUbtxtXGcKKdmMHwSf0DVfAk0VFH38Par1hVgaxYLsqZVplXZ8oS0L1QoBOhW7LZlgAmP0L9xP1WHXO+XgCLsCqxbrF9eWXdd0Ft07/5boUjROFYFhV6jCiaaePBzLm/50foD+6KAi21N0p0YE6Jf66zIORtKf9GQaBQTIAMcUbLQkB8kCRhUo+Ndsyc5WV1r1BVns7Ca1vVZrMQ5AuV1tbjQp8yQ17SCkgxmXA3RVP0gkCdWqx+2tUFwDcPJCh073OsvE0oVcEcuuuF3EDKBrB32dRYgvzAnQr9u1X2pIIg0c47XQ3c4LN6EDg8TQv3T/aDcT9xtdGvp2mwgQ7qtqC3hiqqMkJFFYZ75bU3ZGC4Jm1kliEknLSR3XwR53gO6z7J7sejdfWY3GNMoB9iz51c4sKcf2nrSDrnv/pcygv+bETQS+jD8sGiayANX7gsj19Vh2lUWOLTRkZ70sX7gUyKPboZzcjuTJAT4joi7MxDUGHm+X5Y3LFTlUF7mlC4eOWM71VYm7BxGupCDDBDZqBXlqXCRAf3IMa0cdmWLSaYgKbCB9U6DZFqVeLdHDHCMcOKO042/rDWKJuH+Rdvpx6iFuwAgUkLaZxAToexoYcwHVXYF6q6wg4pfumQ9k3njVGySC24k5POQBOujqGpOV7eBgnbRWUtydbq+0cYiv7W3UGMP2NusEXTjbvAAI4A8vb8RHjAShOw69DohmIs6qor+y0JjYpwCXMJF/UrFSA+bYr8bawDoEcMiPiDjrJQWbaeVZbUYLBOih3P3EfMWNv/r+75R7LlySS2OImWl00NGFRKbGkMhGurSYoevTRt5cLMu67j5eoueormPVqckk9lm8MOVxd0LBdc1+Nzt/hXoFZn1lBX/vYuLaup8Hx/xgTWfnrxcoyYBgrMVaVZ2CMILjDCw+2/KT935tZqCUMvze8+PmgAEWJOJaLBvBmF30tcmUQHwCZktSEFiWQpxuZP7wXB/IR1CwhGBbbrYeDCwV27XPbiKZ6Jzr+tF8o5noaAbTSmPn4XkwX4/rOaA1HrrdAdcwmBagtmNv4nfUOSOUyATo6KhhzAZY0vF8NTtKisOlKxjHVyThmdk85P26/fhN1zNeOluEq8661nluBPNXd9O1fWTTh+n54cUX/wuOjFpBAgwG5GBQcXfxOAf6blGI80ULOwXZ0agK7GJZBDKKu9a0dR8zhzFBXV2Dbsmpr54V0rMMFGwFL774msQ6+dtnZ9NlSj/wlf95Ra7AFYD+ilzWl/akhw4dkr/6q79Kvc9f+Gygu8Nm7fTp0zO/UEpd3LNTpHA5QI8gjJUD6KrKmbCTCroN5qlIVzdPRdW4Lss0KkoQaodVZ21xKKnXL8ACq+xeTfUBMQuqDXbIdd6ZM76WOqgJFrIXPK8mGAhOOFDxzfklgBFQiuKQNOx6DYq+UOjVgxNBHwWC9cFYIIYC72cevGVUI0XOziiI4dfuP3zn8ZwiZ0YpygBIOhaczj7mg6F3En0uMu32pGmTB9aMW/DCqj/+7EFaO+gWfEmfNG9XO0C9a+Oz6OwKWsJA72Z2kkAJV2Vj0DvbBtCR8FJ8Dhe1ILI1DeXoR78wd/Kw8RPvpic3tA0wYw5rNHTKt6eRnB1P+PPcOCCVfiOMZAAFd9AynT1gYivoKqFT3gD1s1CQtgk/QRwOCfbeelU65TJ9zdE5P9Cs85BbRFfT/KZxxfRw084J7h8t1XIHvov7+QGMzpom0FiHlhrYDDrfos+jmzuCq7ijCQRBpRs+/MczXbuM4v6j8sigIr0QYnchgTRU07HPxuGY3SqAddDbt8Y9GcLGBp3ZCAJoMTUYFptd2dFeksXWsqws7iMwL8KnmUW6y8cOLtMIsOTb09N8ET6d5Ta/aV+T6Jpr70RRkhfvHPynAnHWVTeirBXWjF9pFPeeddDnBejX7txP+qUmXIrxHKC76rknRM42eGGC5WDZrXA4mznVAmEebPu1QeIF31usFReH0k47NAEwt+ufMaPNpmKOBvpTCm+OdfDCgJ8yGyzhpwq1PUhBtVLc5+6gX3+jzphaQojnbCPGisiBWiLdUiI76yVplotycjuU04NQnhuKPD9GDEaiWEwtCm9aKMt376rJ0WZBbl0syXgay8U+6KuwUgtZhHtqmMhGKPLQoCj9uCCrUZnex7Aw9CFoL2CkwoxgzUBArQwrI4j1ZUwXvRgFmYaRFmDoQBBLXQrsoENosR7H0i4X5KombJDQTVdwDEo5zq9fmlMk7qYDt0rEwgIwps7OYz3gntDWMtUJyQC6OmxQnYTf6OIv18uy0qhRNwOxGQ4TXvxW8AxxOFWDRmHwwlgLnmeG8CJPZMw5YKUrs9tmo1QOzD22KXjPRrVc+8BBff5UykBANrLlLAsU2Uu09QrlK6fmo7j/3T9/kzx4aVM2Jij86cZ1nRqK1zoV2gpHXuzKdzS1WKbvz0eMsg56fhQkIw2pXoRJBpjStseC9Fwg+DchQlPfd0agQyDVi9GymrMPyHaxvezsJj9t2L2PEmmWYFXXkvfdPVsXGK/rZ8X7b/+h1BoPVGzErD6p+AlZaBNcy0KJa1KFWLU447PipP9jfIJinco6gGgmRMu0M67ROqX05xwjAPxqcJTA56bbBcbDNFPE3kLsHYLNQmE3fW+KdnVf4vdZXEYxCbkkX0sLv3hP+Ma7gp0ivrwDzAiFx0KR/rlH5jpj/+3Nx9Vq06rTmmNZZmVsLx8d4nhHitozATbfI/k47Y9NmZIsdmks8CKrrzk8VsX8sueHWC9CsP++n2PewHHxR80xNQfx8Sl/H9515+fh0WMtHfsH/EB8+G+nZrPjnBmAXPmFma7AFYA+0+X61jy4Xq/LQw89JMeOHfu6L/jkk0/KjTfeKKPRaOY35EH8+J4dBOi6nzVZxLwdgq5S3J3GlTD5QlcC4N27tTiAoORexnMk8NzUOSSAbYJj66AHUxioeW/XA0fW3YHiMahLS5wFRoVWgwd9O0mLZa3bqOwKPpHAoOoK+mE/BLibMDkrVwA8E6mWtPrI2aVpJJf6E0auuiud04JE5OJGb65A/pE3nTCA7jZruW5NRpYmbc1BuCc5eToSwQpomzbHp8HVQbkp2zO50mQC5RQcRAzsmjKnVdD0YLDAf7koXCYOp6ru2k33DjogGm3WDKgToJt/OBSC2bE2gXKIxB37s3tmum755GH9J+6QYrlGWvtXN/us6J/sDQXCb6tBKIMwJjCHynDAbonOqOM//UyYmYd1HoB5Is0iaKsiDUnYMV+G8nytKtcvdmW5WpVrF9oqAocZ1ZSSoPPTeghq9dn9bZVlkCV07ofthyCSDXyrh7ylbF50UuRgXRHNK/0wxettjgPZ9+/nU3H/X972XkEHHQCdtHwTqsPzsxgGd4Jxn2B9dXtNtkY9E+EJRWIIyQWy2FyS5c4u2dndJVctH+TqwR7TlWTw2zthtg49wPhfvzDg5MXWmDDlMg0VtdGxFO+gW35gc/7WGXZBmzRZcMs99UHfNoB+z5wd9GM791FDQIuLuhcWqjXuYWe6+OfSYpYWFvDuVZlYE1UHw/jMAMJIfnVfG+sgRxfEa5DSCbG4SkU7JtZJR3z1opomZPo8/t7405kJenNshMV8ia3jmi92ePKG9+tFEl57KrDPLxJ346tuYPGGnUtDchgZgW7FUkVBbKmoAx5nhpGsjTHSUZT+tKixLYG+A3q4odyyVJHv3VuXV7WK8pYdFZnCNq0PT+pY1oJYLkxi+dz6RC4EIl/uFWQQF2VcrBKcg8nA08MyccQkJuQFMHsKjP9IYru1kjQrKI6UWfT1RJVJP647CnJRxAJDOylICQr+Ycj4cLhTY1ypFnU8qiyqsP9L987ng37zge+UEEAIAMg0G7y45wDdNRC8g47zj1Z6pBEHsq9Vk0OduuxrNeTYQkep+pgHjlAgmnLNqK+70qRxbq6O9Jo+M0AnPaFmB0pJaqlmyu5Zxp6CXRU89cKvFi95BpvYHsGm2aGy+GL6G+nZk4skFZx9cSgnT89ns/a5d7xFHsH5bONBeDHXXAGt3Kn9TifW3arxmLsnRz/T0bo8xV1VzNOCghV987GNauY5gO6/78+rPCkV8FNnj8shkZ/33qB3vQjVjslmjnWP6lk0DROyuyCm9q5Pf2XuM/aWW75bRha3EGsoUGYAXH/ijWvjg4UjA3V4L8rgKHF/4NtPM+y/ehkjkWonml5/G7FIC7MAnxGKKgDaOtqlQx44B/R0GKOolGMM4vzGY6BbgN9TWj5cfeCMYmONyASRc0JIl8DdzvDcOAJDJVTkz802v+858U++5gTjtK+ky9aDrRZ1Vsh1qPP2oGl3289Uj93exdaYRe3i3GhiehbYZymhnGHrD8/E84WFOGVe+db1UY4MoOs1URaJW0fayZ0bvdTP5dlJlqMgRn78yadmXncvzAmu/PnluwJXAPrLdy1ftmc6evSo/OZv/qa8853v/LrP+bGPfUx+4Rd+QU6dOjXza3owetXunWpPhqBQ1EovOua0hbJ5TCdWovMINUkECO84IlAAoOObR1Tic0gIPqrSjiALX0t9DU2WIFCldKcK50D3tducDyXtGN0FCxyqjqoAWJMBpbK7SBfAOQD4xmQsa5ORxAWo0AOcJ9KsmnAauozTSC72psS9DXZOi1ICxToROXdpNs9Hv3Z/ctsN7MbiS+eAMgqqQRJLVTwFQPVTEwz3ok07EGQVm2USnsiq03gsExH+DpKBhEmxA2x9dX1+F2FhipDr0ngATwVzTM0dz/MPADqtUFRYj7PcELqCDZGpI4Pmjq/tKJTDH52f4v7Mj72LnuXomH3FAPrpMRRkY6q3I4HoY4bSrgU+EAExZrmoNK/q7LVCTJptqxhzxhxWSYuVshxq1tklP9xp0ZN2pQ6VZ2UFaG6vKYemHhk9DNRgHIQOjnxjOUAnmLduAD1uMf/Oa+KJAhIXS9KM6uwFdj9QtydT2fOhOQH6W98rsFnrh5pEOcOXa8DmIifhhElRb9yXYTBS8Tio706HEkwH0m4sSLe5KEutJdm9sMcS7PxwhINQV671FZYPMzmonlv3vhgdNLIjklbrdZ2mYnA5ep7rVuSBftqNKugMes9E4uYF6MdXDjDm6O3XdYDuttuo+X3CI1xpWa+rJkepOGWOou8Ud/1cOcq+xQTtkioryO2hQHVPZ8YtsfVEN9+Fz9PTvaiE95Ym/fY5vKNHVW0DLS4Sl8IV66B/+exsnZF0fv/aE2lCjtiF54WoFfZAt4rP5kXFRDYnEGuKZXsiMoStWgxaJrpn6CNHcrxTku/aWZGjzaK8YbEkRRQmQ+0Vg3K7Oonk7rWRXJwm8vd97aBvRBWZomNmgJDzuqyDKaPHxa4QnxCSW1XErIJ0MK9tRTkUXdgNRnKPxD+OpCEFWcBaMIAO1s1VTTAe1IqNju5wSvj/Rhp+8Z75APqrD36X2Qt6IVdF4BRUa6EUsUkLMw701A5yGk0kjCeyp1mVA+267G015ehCh0VpUIOpvD2BGrR2zpnPJ/CtFhnHEKsSWZugyAnNFRWNo4c3/KenqnngcUNHAeystdEABeXeUTeNFSvQkwbPvaAwVc/mLBICGiArAEB/7MxsZ4Wvu88ToG9TvwN5gJ+xiCReNECRIuuk24rnhTCNcgtVHtNZoDP6ufrXpNEqN4tu4IqMPy0O66y6e9I7EDdrU65FfW0t4jHKpCKuXuf0Ap8CdFfN9sijsRE+441SWXY3G/Keu+6fGSj5tdt/4lYWhFzIknR6qsTbT8vHsmKyjQUAlFM4FsySRCpmFQp6OQvjZRTDihJRU8TyDptF10az0S8iFIWRt4DppcUuB+i45iEKUDkGIO4FveDB/mFH3drHWnPSMhBt4RKpWAyC1aJ/pYwa5lKRbK6emunapcyDG68jU0Dv4OVivvaXbBRkhVp9B3wnpm2S/tnWgZ8j/shsjegV8MJGfujExzT0iqJhpiMAvemEOa+KkJakUYYwro2WapXFigt6P9P3kj++/cz2+2X/hj/iuT988tGZrl3+qa/8/8t/Ba4A9Jf/mr7kZ/yZn/kZueuuu+S+++4TdNPzX+iav/71r+eMOmbRZ/3yYHTt7p1SKaFnqgkW/lNgngF0VocJelUNGDRgqgmTvoZ/Q61PlTtp2cbDR49s0KN46Ic2f1XCHKYKuKHiv9JsEqQfWVyUBiqzGqnS6XQXbXKgnocRiD0IJpiZuTAaytlhX8ZxKIMokEZFZKGmtCy8/9E0ltVeKAV4jBaqtAqrVNBBT+TZi+szBaNUYfbNN0oLw4P55Dx3I7QgbekzAbceluykmPqmBm4c5CqgRCDulLiC0scmVL4GmwAdKCTGOvtcseTBO2sAC0zwDSCmIMM6uq5eSwotkkJT4XegWQGodyobO8M6cgAQitllXEda1hVEemEoBz86W8KKz+rX7qs/+kNy/0aPiuz3rvdkLQilRwo7aGt6+NN2DkrL1q3GjDkKB+iQ66w8KKoYWUikXUwIzOHrvadRlxuXFqVTKcueBpRkjYrIvMHlos2z/OsAdIr0ecHZDjBX53Uw5B10AnSrtuuqVdIjAaAzHDw7MyGlQRDKrv97PoD+K3mAbnOBvuSclq1AURMjAJIEHYw4lO3hlmyPNqVebUmj3pFOvS3LrSVdv54kaGvSP74lnd88umRFiKxr4DQ6Cy2awqeKsRnN00E5C3qWsJHQmCJmVXF/qQD9NXsPvUD4TpjcYC94su+9N3U+UNXyNOe0vekick5PpZDRC74c2GDrIc559w57EEI/eE0kWHg9Jl45oOTXwMWkAPAxguKddcRnn3vXe6VMDgf5XlDgv6UdQ3zGWL54Zr6k9eix42QxqQCTgjoHly3480LEyUW7CIAT6YNiDXAYQXwPrKaI9kyHGwW5qVOUg42C3NwBldy0I8plCnyicPeZ1Z5cmiby+LBEcbiz45KM4gJHX1CsxTnEz+axTf3WtJMJ4FvBeYUzoCztqopA4tsXGLt4UUQxyR1wgQBAn05JG1+pVwSxEPPhoDXF0ZT2TT/7+dnincc6AHTtNmb7DIk1Roao4s5ChxfbdIMoeyeh+ON4OpKVZlX2tuqyp9WQIwtdqlWvDYe0x1odjLhWUfhRkKDsOWmLxwAAIABJREFUj3qlwWsyhkYMTg4wRVAEiXS9bU8CFs51tMrZX3rmYEDCVdp9Hl2Lk1oE8jnYzFveO+m6eh0EY4UDoD91drZxKL929xhA3wqgTWD2gbbX2DXPFbi82GCVG20YOEAxNooXvVxHAetRvzzY5P7MteV6EgrUqRBPsVtlt/CKuGBO+hyZUFoaFhj4MiCue1SFXUEpdkYR7kMQKsV9f6sp7737wZlyk/wZK4dfq4UrDQT2VrRBop1V30MW8e3zcf9QZBWFq0hKYLGFUwp/QucBoy60NsOazul5+NmI+XFS3wG0OUph3ElS5RMddcHdKSsrz79IXcd+w5qkToTOr2uzArcIoykRiwZVUtzdxlsLJ+xKp+d5JBvrs81R+5q74/pjxkax+21FGS+u8L177LE3n372tEBjLKccwPdT1Us7DtL53lMSfXaQpFcGeXmSyBb84qNQLgz7FJpbrje455dqTWnBTs8aDt/otM6udPYI01287FeQc//2V74687r75lnClUfMewWuAPR5r9wr+HsXLlyQ1772tVRVhpr7ddddx1fD7Pnv/u7vMgDef//9snv37pnfhQejYys7pQYBNgYJTdNZYadYiHbKKWBj4Iz2KjbDNo3UHgehiP8xQdJEEYc4jgZ0Y5F4YwYWSUSzik4mLCdq/DPADRLVaxaX0wSD74QUTSR3Sqen3yQPuOyb1T5S2GNZHY/k3HAgA8zeTsdSL4t06ypmB2o+5hxX+7CfKkgp0Xn3Zr3GwPfkjJYSfu3+8i03UU2XZ4dl8S9UudYk0urq6GLZweyzXj6vTtBunQr6qQIMGCCgUAo66bAUMzE0zoDlCO7p/LR5l+IaqpWP2hHxLtkB5uwDVbrXOXSODKBzTnaDihY5MFfLKHQBVXgOnwdqr4c/+tmZg7hfu5P/+l/Jw9sDWZ0E8iUD6ADpFKlJkOwLgTq60aCBcibU5stbmOctFqWNBBwK8/BNrpQ4p7mnXmPn/GCryUMWVmpZtTq9UXxeTeR0RVEQB/6psGkxAaW0I5pT3nYWggJ07ebjp4JzLcLgJ8GUCc3kq+J41f4klN1zdtAdoJPibl03L9L74EiaahrzArN92IPjYCiTYCTlMizzavQ7b9WaaXpqqbXlcyTw+SW6LIX9RsFGt0AOoOfos95FTwF4CtR9fjBLvgmK03lrvU9RDIAOFfexzNtBv2XfYYsepmif65Q7CFfxnYT3zgGypk/6hREI7Me0qMDPnOW/LHzZeiBwt1l3p7ZiXbgdm3uyYwYzL9jlQMPtotwP3bv+iM8uAoeXd+FHqhnzXuevpTWYrYN+35wd9CNHrpfYQKazhdA1R2xo1mGlhKRdrxOEwYBGBxPoI0CwriCjSLupEG7cXy/Ktc2S7KokcrgO8cKCLNS0CNGtN2Q7jOTk1kh6YSJnJwrQnx4WZRBB+0LPAcQ2rnsD6rAjI2iy+VZ0zwHQOzUAVwXn8FfXKS6cATHFqLrFoqyAAh/FUoC/M1Wmsa+hpA2RUZxdAQH6z80J0G9kBx0U4iwSoEOn980V/y+3bXTDSax7fC/UKrJcr0inWpEd9RpHK3oBZsvBVIDoKWKknghKtQbbTanIU3qXmMUbxQi1kMu5ZHiiG9BVDKnnTjaKZSNA1rF0UTNldWnc1JinHfQoHYnRbimuMYqDT5+/d6azws+JL/2L75EnN3vSm4ZkkqU1O2rcZOwWX/OK53TEKAUsuRl066lbpAYAVLDoIpglIkL90r/3TqnCGwX4WlTRv8mEWcHsy4MgpUA7Fdlm0e3peV7YPs3YLlZkM4r77mZdfuglUNyLR7+N9qMWovQzJQXuAddfSWsG6Wy1iaZT4T5h4QrjH+jcYqSC1xXaB2B35GbpPc7xpzGj4CSSFmRTJhXbFLo2kEsgX7THZzcUnQrtuAOka5FUwTvuF5Xkc3omnttk9REdN1rfODfXmvu3r76esYj3O6ddYOlC7nzMTkK/73pa5VkUzqmw2GgrJDuzfZ3lTtV0kevZguwPOff6eMg4dGE4kGEYyFK9QT/53c2OLNbqaqFrhZhcLejy9Zxb21kx/LIVT3u4X79vdu2D3Ce48r8v8xW4AtBf5gv6cj3ds88+Kx/4wAfkr//6r01JUquzb3/72wnSDx8+PNdL+QF4ZNdOaVbg05spkeLwZ7LHWRdYt6ggiCrK5pNVVPg9AGkHAJgEhzZo8gAv6EjgkEIxoVoqy3KjyU75rmabwXttOCKd9MTyDhVTssouDgQmC6Z0TJuSHEDn4QYAz9dK5OJ4LM+PRrIZjGV1PGD3o1NXcL6zUab/88X+VMJI7eDKxZIsNyERJPLQ2QtzBfJPfffNnNH2w1wpcJfTgzk37RQ5B+gpTTXrmFMsxQAVBH4AyFF8AFiHCjg7cqY420AyashSwaVS4D3x8uRVFfO9pGEAHfR4B+GcQ9dZfnbTjWqZzaebHy9BhRZfABbweaDAes2fzW+z9vj/296Xxkh2Xtfd7qrqmu7pnn0hZ+EyIkWJ1EKKEaVYMmJLjCDAMWxksWzJ+pHVP4IADhJEWRArQBLLCPIzka3IiRdYso3AAgzHDmDHQRTJEkWbliiJFElRJEVS9JCz9d5dayfn3Hvf+6pnhpwqToCa+DTQ09Nd23vnfe9+99zl3L/5IXt+q2MXOz175NIae9Bf2NhmZn49xOB22B/XsPmGk9GlBkrZzfbBkW/M2pG5pt3cbtmxNgj5Hts317Sj822eC2atVp5JBkjySuUajl21dJaQmQLJ8YoNV9flIolNE0Qk2wIQ1OD4FCq2Z99mOIck6D7ixF3ZehNc7/bsxC/8ykRrDgT98Y1mVeLuJK52AkpjwCxJZBjpbKSTRGfdTz5LMf0UU8m47qOsEi98et33Vp1NYlj7tXwogxvur9dsvM6gpwhVEPoo+eSzk+RHuR4O0jPoQdCfnGzM2gOnzlTH5W00ec18tBnOGkEwiiRF9pnBLBDucAhx7TD/PI+zHp/m9xccO6jy4vkUgaMN8+uPx/J98dmwg16R5IQps3spVLd72oJfrxmKKqGnNBcly05nUdbsc9DpeKWyb54LS1sn70G/9cybXayzojaoYgFumJzhBD1nhc0OUDm1Y5vdgW13h7bWm7H1npOCljXscGvWTsw1Wf2yNANhzx3bu2eG4lNQKkZlFnqOEfxdHzRto2/22PoO1/ylHoSlMoOeBAozy73PP8vE97Rgs2Zsb2uGAQCQc2T88TiFqiD+ZmYHGg07NdeidPaw03XNkuGQAcFD7RZJQq/fYZ/3T39psgz6m0+9NwqpQ7E/enx5vUMgCjaEglHRvuSOvo9WwzeOH21GGfx2m5X6GdEhHIU/0F/h/R5ZxSEDhW6juIbidSDn3EdjFF1WJvso02L8WlQa573LcWLR8lFm0EnYk05Fbzc+azjs2YsvPzyRvfvTv/qgvbC+ZRvQeQi7XfV506zkGKk6+OGVYmHfCrvrpepRdp42ptKVSBG0tKdu9rOH15+erUseTPG91cOy1YjTwgBXWfKssKlK9PP+LCuK3BjjWqBCBfsXNFQe/IPJdV7ad73bGkHQcZ1xnHlvokUM1Q1InFAsMCp4vA4C4nCIfrhYG0h6hl13UJYOHw+aGbGqI9TNhEwYxviREufRiJdJDBL0HSfoUb3EuUFR2s5mSQThog2B04CGaI/xCj+3g3WqhkGp1O8JrDFz/vzquYnW3MceuNfa4deVO2sZqI0TLa52eeEv/3MZ9imDONX7FH8sC+58H+lyXzq7uc7KmVc210nU97fnuYfcunTAjs3v5f6CIB01IvIDPWI18pXXy4Gs8YwtnkHoj395/NaKK4Ohv14PBETQrweK/w/f49KlSwZRONywd955px086KWpk35VBP3IYduLMQ8kEx4N3kIJHBU2QZKHdA6QUc1RG3lfb/bQF5NOhxPRzKBz1mxB0DGmBgT96MJeioyAoGPTeGltnZvm3QcPkaBjY8LmlxlNKsoHUUfZZG7DVNUmQUdZ9I69vLVl39vYpPN8bnvD2ug/bM/YYgtl9E3rQIBoE/3qZhtdJ5on93n28OHnJou0fv7993HudtrAJEq+seOd0Z+ffWvuPDr5zvI2REe9pL2LDCd7+/x3OttB0DGvNOP0cBHaQdArhynnp7OdwK9B9i9hA0xbnc4rNmb8n+S8EIvLsncEYdzZ9T7nHAHCx2PUEjLo935u8gz683/nx20V/ea9gT25tmmXuj17enXDlrt9O9vps/+8F6XuUFZGv+shZMmbM3ZkrmVHWi0S9Jvac3ZgrmnH2i06sChr93NzMlSLntU48MrkxhSLOfsvswcdQkrZa1tGmrP0mT16mQVjKXS8Py989i16Vgnubjq/+Lj1bn9ikbgk6LVIXB2xj5RPtR97ebMHDPgjsxvFxpz/rfrpdokclZv7Lg5e7u91ZsuXfVEaXqSMihL3EaI+kjGvgwC7e9DXswf9yf8xkeN1/8nbI5iVGaRa/TerTVx8p3D24/4lxZmZYeYaVRZxmr7WSIDc6cb/UXaI8+NEgMhq4/EkFmyBQKtKlNenDU9l73zvEeGw+Ay8DuXtGNGTn1kR9Kh6qo6/qkLwz8N6fuSlyXrQb7n9TZXCv/fjonTfg7awtbQ5kc3Oqt+NLvqch7bRNdsgQW8weIbRjYfROoMxiJCnmjVrNr06Z+8cJmvsUBkfVUbbgwYFz17sQFTKv5HzzF5gZvSiwidtFpY7bAEIOtTcEaSFv809LOwdJj4szJodmJ21kxDtwyz7HiYhQM+kx6AlBOPY5DDs2Xa/Zx97aLI+6rtOvsd75SNQ5dnuCCZEiW+KUmZ1UzrRCBTgPFABgHOi5kEEYUKysJpBnmQCyvO8r8PGUfk+imirGHdmv1HRNZJBd5Lt5ddxHydBj4WJvSifw6ovrq0YrVrd+R4FgAUEQX/+7GQE/Ykf+6CtdDFuK8Kcld2uWUdWr1X7XJX19DurpOoVJmmAqmCcl+W7cn68jmbT13pmJN2e1vdyRdCjNidfm8eUWXJW2VSm0P/jpLlOeFAMjWPWdryycLZh7/id8Ub74f3St2tnBj0qI2DVZllGjsCPt8lBYZ1tcXlsuUCIDzLWTpZ9H3Mwh9E7joCJF1mVai4u5sanVmnc2j76WzhBT3uRvo0fi1e2sLw96zKiAowVmXjPAJnJibi+zLSH7Xa7OLBzExL0nyoy6LmCYgut9rm8vmm782fetxkMyr3QUarerV6XZdVFxMdT+tf1RJCg6TLweiEy6Mudbbap7Gm02Cp6274D9KuhW4A9JfewXFwZhK/vhTyQvG5FB8QOWq4G9nN/PP54v91Y6Pfrh4AI+vXD8oZ4p5qgH7L9bZ/DODPj5ZHrmJ+MPvMg6AstlB+6I+obmDtFcMA2ejC2PkrJR8O4vBsirJz924CyO+ZwdikGd2zvIqN+RxYWGQR4+tIyP/NNBw5S3RjfcEwZAWTZt5NTzr+MOeH8O6P8Q2ZZ0Lf80uamfXd93VY7IOibTtbayGbN2onFFsvgL2z1OW5muWPsA33zoSVmU/77k89N5PB/6cH7bW+IxNUX3Xctd+g9Ku/RfMcNwYR63rlZL0TgOja0TZQyxeMg6SDzOM/tfkSws7xuxntXU2k3e2JzA8iyycrV4IbmVRCZQUpHlYroQUI47zmyrjn3Fdkb75V0hxJ/xxdKDn/gv43nsJbOw/JP/QT7puCEX+hizNrAHr2wYi9vd+yxlQ071+lVPen7WhhR1bATe1p2tN20Wxbm7ZaFPXZkbs6Ot+c4Tg8q0iyx9GI4qkYT8yRPhRRQukWk0qExVWaHsMHBUfdAiTtQmRFOkoUAjxN0YOY9xuw6q0rb3XNwp8EnGOToHhD0k5/6pYnW3M88+BF7bMNF4iBQ6JejdkH918LJjN/9RxbflSbKNQvyfaqMeTiOGZy4EjnPtw4/zY8i/afC4Sszuu48pVOa/d3+kw5gRY7dkaRjgyxL1YPesYcmJOhvv+kWBr1wLbPKpLpnotKFRD20IrLUHkeLkuTMUsOejSJYVw2xhL1ac75uXFk9JgVQ+Muz3Lyn4IiHMCb64dlW0vA5wWgFqkppmTJynBgkiKoankuU1VOnIvs+o+w+M4l5HA+/OFkP+qnb72LVkZfohy0ITRJk0aFRkuRyQCG3WVvvoox6x7a7qLTC4zgfzBwH+YhycwYyzDY5+cwFQLNvleskROH6bDGfsblWs8o6c/0xKICydGTx63Xv9+es7Ztr2F6OhmSNi4s6or0JFTmzM3agMWs3oypmuGNzgx2KkX1nbYtWBFU7ILvoU0bVwr95ZLI+6jtu/j5eS//yxV8TdN8ZfH53XfKe6wsaJwgw4HywPrYGA1uNiSgQTaW9od2pSaM33FSfFnrrUZkyQmLCtkUG3avSPEDmGTi3K3n/Vv+Ph3KP8qBBjITKLHMQVvQMg6A/NyFBf+WjP0I7GxKzblN3pwR3eVwVwY4qhDILWofeClsV55NEs3y7XFF1OMDxqC2pZ9DdTtaEpwrqRpAjBuRkrDQwra+Rn5cfCIoPsFdDH+DQZ35nrH0C71AR9NP3RmDIbY9/QLTw5PqI9dCAbkEU7HsA0dcpQ89hr1mzg+OMQBxnhftNGtVVbhdht+n7VECOBoi8QL4gh/F+EIajPeMc9OhV53G7OBwDzLQRXgHmYnfRSuGbRx2S3xnayxMS9B86c4tXBBVfqeVRQRh3Rv7ucRvYQS/xd185lPGL/vK8xvQ9gpDXH+V7MXwwnAsCcfDDV7og5Ghp2SZRZ4UVdSDcPoKgH1tYZPsL9Jw8KBTXouiJL++DWGnRnFIT9Kz8+o+PPj32utt1G+rX64iACPp1BPNGeKtaxf2QHZ7fw2y3DwPbsQ0Q9CgrQtw6nZ/siUyC3ul7ORZdhFQ5ZckmsvAeBUYJOEqotnveJ9dswAlt2oE98zQk5zY36djdffAgZxKjZxi/Z/mez5/2cRseGXQz7Jn1ITMqyLC8sL5pz66hV61rl7a3mPXfO9dgb+PJpRaPaXkbBN3s4jYIesPedniJzu1vPz5ZVunz77/fFhpFZrZgMZXxzXEaQRBTkT7PA6XtwLqLyoWZaC0Ih4kZdGDJESleys8e9CYGn7ijzii8ewzVbF0n2VlqH6uRG6Y7s/iJLSDJuf+sRWO8fzp67YoZ6tlHi3cEQf/R3//K2EY81x0IOiomcJ1BzreHO/ad1Q272O3aE6ubLHlHvynGxBzExtNCf3nLjrSRNW/zex8V26GIjWoNd5DYB8ssmxP0TCNlhtu3rdgmI4DiBNNdsBxlghL3OouUW389SicJG/u+6HwnQQf2wfozm8RAV61evtZBBv0Xx8IucfuXD37EHl9vUemeYoEFox5VbK1LNd1rKrLpu13c0Cbwp5WuaM6fvXaLlhmEdAarhExV+lpkjCLrXLlWI6Nq3OFxnlCquE9O0N920y1sXXCCPtpL6sS8Pv8yi45DoN4AqmWiusUdrLj3s5c0MkYlKWabTjwOYl6WoWeGHOXZFI1DwIfCjFDl9bLxnJmegaHM4nulh5cn572eJLzK7kTwjfgy4DewhyYUiQNBb4VKONXo8d5RogqCjnuQ1SV08tHzjAAubNSOdXs+OoojEilw6crlEfliSfu2F5mQoKcoGR3w5BUg6BR/g35ICDCGjcPfF1oo84+bn9UJwA8B2gZ70Ek9Z1C6vougz87YCTw35javdPr27dUtrhGMcWTgAQR90LNPPDJeuXHes2+46S+yeqyiu+gBjionJ+b11IAk6nnHgZxj/0WwASQdTjvspQvOReA3qgh8jVxOXzMgmRUpWDO0lhl8jMB39Xtkz5OclsG5zCAjMAKigEAR9tYMIoMwhLkJkof+2b49M2EP+rmP/jDvBw9R+3tH13gRhhi1T1UWs6K81Uvr18RJufB6eYb1e2VFHM+neApfUpnJiopXBD3foSJv8fEh8l6FFzLB7Ou+tote6TRj272+HfzM7461T+Czc93tOfG22JsyKuoTbvL6FFsh71tfPXWwAevFSXGeQDgzVcWQW24+L4i/n3uQ5hwVyzWR01Ow6KIRgpnxFFzLAJLrAuAzZ3YGQcwx+zzG2fnReyY/Muj4C4h7FYDAEQwH9srqK2Nhl7j94C0nGIys13EEZEa3xpFFl/d2JkEywJpPIgKVFoI/29sho2007DN+x30OyNd7XbYtOkHv2wZ+jwlL3tKIxJXZbfsO2rGFvU7QW60QcA4PKHw7+Cn+Fdc4NmZg7PoL7gXB7qHa9NOPjecTj96B+u16IyCCfr0RnfL3S2P0zlOH7dTSgmd9osQdSuheau1iSMhGu0pwlnqlgfEeXLhcIOjInCDjuN4bkGC1Z12kC5FROKfoI3t+fYPvA2ePyrNNOFBNe8fRw7Z/bs6W0EsYvdpOakL0KDaCEeJuQ9sY4HvHnltbtyeXV5npQJ8OR4O1Gnag3bBbD7RpeDZYkm92bmuHhO8vHFvkxvQbX58sq/R7P3gfs5hVz2xxzbnhkfi4A+1OepYBxibDsUFO0FGw2p0BKURGKcev+Pxl4AnhjgvbHTq4UL7HNnp2c4PGGDjDkT/QbhdOfeY1a2cio/7pwHAzqcrdcX19DE/2LZebSiG2ys0DlRP/4I/GV/osCTquOzfuENXC7PPOcGjPrW+yzPT8dtfWe32KIqG//Eh7zg7ONW1htsHKDIohhReU2Qv28aHkssyyFP1qrmPgrp5vjhHMqNxn3+i9L7hwmuLaphALyAkj5Nj4ePy4f3xoDwhlFgU61nW2AL+tbvfs+M//wkTOAwj6Y+tNzkH3DPqubHlcvyqLFJ4kV0NJ5ou1mtmB/JkPASkEPNzluravKu9QpBqSMKYrm+Q333Pk/rmGDPpXnvrDibB7y/HTzERgDXi7B4Jd3ruc6syZnSgJMRAGWab4W5wXKycqEcB0fTxjzrVTCV363/CZ690u73fYKDyeJe1VNjWOCU4WnDSKw0UbBY6Hgo6zUER3kbicT82fmdWP9ZDHnwEcHDaqkb7w3fEyI3m/nj7zZo5X4hSJZvT4QpSMow/dyURfN4j6YKZFQbmtHsrxZ7wOFcruRUY2W0po31EeHWJjLPOFXYwqLASB8ZWOL6ZKgMTO4VgykJjCbtyj3OlHqSdILYK0880g6ByPOGttBEFQwo4S98asnUCG2swWdsyWOwN7YnmTgVFKnzFzh8Buz/791yYTOjtz/F22p4m6gZKEORqcjMLWoaiWKNWhMT4S+yNGXMbaIBlmuXdBxatrHnbtChnmJBAe3K6FBHmPV1n1IMDcd+tpIB6QD+FLajIg0D5LbZRLHYx09GPKaSQMVWa5Lva0Yc+eeHGy6oPzH/2gLbaa1X2HteAF0lfPpOe5ptDe1SyXc7qRnPplpCvLg6/OzSorNmInI0xRXYkqcLnrYMpXZ+tLhJZtvdOzw58dT28Db5/37MJRzPNOsTPf7/pIKDAL7rsgq+eCnPMvkc2mUj/WPkXqvXWniXsOtrLRoL3p9FwnyAm6ayXgC/cgzzcE97A+8IXAHtZxf9jjc3rQFSDx9j2sWjPsew9RuCFIevyfwnWhI5GVmhlQSD8gJ5gM+nZhZTJtobcewWQjV7vPtsAk09V+HgHvmnRnUKKuOCw1R7zKCZV23l5F34NBYl/JuCdXu9vEiS1StmOXtp2YL3c61HRCa5Pfu47zVt+z6Lfu288edBD0hWaTWXZM/EkRYM6zhxZBpSXhlZloX0V1S95JtA3UQBjYr397vLbPa/MO9KxJERBBnxS5G/R1acTfe9thO724l8YTwhrMtIQADHtnzTPhMCaI1iVpgZHwnvRUcMcYGRjfWZKrFza26AyBcNP4ktT17NnVdTo/jVnPGmHzxfc7jhymUu2BuShxZ1+cb6CM7EUJGVTY3alwlffl3sBWegO+71MrqwwEQGWZM69bDTu4p2m3H2h74ACllgOzc9vGzP59R+apVPqb35isxP23fuBeZjHro6wXwwhB52ga79UrHQs6otFr7qqoXlKd5BB4I4KKwAay6Oe2tknQMR8V73Oxs8UNDhsBghoU2YtS9DoTGhm22ITr7IKTc4r/RS86R4Ql4ctxMrHpjmZnzdAX/w+/9PWxiFLpPHiJu4/Vc7VzVEJA92DHzm45MUc/Os4b5Bzz2NETCk2BNmZKY5MrUhQkeekghBNX+qlJDrPvL8loioXVJWy+4tiGEC9iAqFyOJ3MocKA4mEg40HQd3aY5qsEoZKSZMlpUmnOQf/k6yPoKHF3h6cuXOPpl7/v+n+ZWysT5emA+8tHXdFxCXrliIYXUWWJ8ve8j4vgBxEvy97jusIx84Qc5mj3bG3jonX7HfvKhCXuIOioSgFZzraOvF/KrDfWiPc4Z9YnFPtDiA33HIleA7NnPZjG1hHMBeZ4O8/SewWG/45v2L+yDD1FzZAtx3rKQAXuCxf8iSkLQczpJGP84HDg5xHnws+oxODiKsZ6LSsiQNC/OCFBv4UE3edsY6oC1x0dTp/44ATdfw5nmyTonT5GsiVBj4wXR0rm/GycQ5S0w/DxJnNbMOBIN5Ts+z6AGdMeUHEiCxEnZvEjAw0SDpuf5NMJesP2QME9CDoWGXrg2c8Ne8ke9Bk70Zo1yIUu2Ywtd/r2xKVND+IEDW7sYE/p2Sf+dDKSefvxB6zN8aJ1hj9JWGp8gPCmXfEstZNiiKzi3JKgwy6hkgr3svOe0JcIW552Co/Qbl3mn/iaZBtBPClJqgctXf+lCmLtUrHGSxCsQoAE2XMQdLSPIWDrAXUfZVcHiEAC+vb4i+O1Q6V/cv4nP+AEvTgPD05cnaDnI6OqI7uAqAjdVRy43Pfi4dF2sfI1ZdhpNJBZlsfz7a4S5cw/p631bDamffTs0GfH09so99il42+pJvRkuXkflycIOn5CbjZtH/20qE6BDwISl6VfzWzpwrVvNLku+TFrAAAgAElEQVQ/ImmQ7Sje1OJfWV4d23pMlvBgFD6rO4AdHNgArXspQDfSJ+/idMiaQ6DOg2TICLs99sCcB8FrqUyGoKvKELREXVg+O5Z/kmvu7kOHDedbBZoYHIsge2FX836pfKosMIhqRuwR+RxvX/H2qbRZSdBh27A/JEGHjcOZrPDewtxzz5wzuBwBDeC8HRMYTi7us8NQdG81GXDuDaG1NGBAAHsI9ikIQfML43xjX4K/7wQ9v3yiA6rMPvvUi2Nhd5W7SH++TgiIoF8nIG+Ut0lj9ENvPGq3LmEkFZzgiIjmpjQbvURB1tgTjfGxkeF1dXKfuQrTCYIyGDZIzr96fpnGCFk+lG4emmuzdP7plbUw2G6skR1AJvW+I4fsYBszrNte4hOZtBQHzhFW7rFTeobG+KXNbc7S/u7ahn17Zc2dXAiNxFidQ/Mtu/PgPHudlzswPiC2DVtoztg9+10E6HOPj2eMErtPf//bfLZuoZrpBrnO0Ga4M1W+M3Wb2XRg6n2xnr0GthQni/JOOvYkqn07u7HFnukj820aeWgE4LO9N9THqpUOWf7fs9+pEh0Rc87aTeVgJwJ4c+/ZjvmoqQRc5AGyVxlO2cceeh0Z9L/3YSfovmv4xh7ffW4iWUkQs+y51mKTTOKcAZz46doHEdKJC5FOQxJ0j/bXivacR1xk0jND6lUM9d2cDmc6ypzfyjFw8U133nvQs4wea5ROT5Z9Bo5r3ckJ+scf/Kh9c6PBDLqPWavrLf16u2OPr5K8+0P16hgh4rleq9OtvdMU07uaXRvNH3kpqC83f6SOodS/785cjVagjI4lyjaF/qBv66HiPmkP+j3HT1frA44LMEBmIsea4QhrPYZaCRrnAbKHeyQF2kAAoZfhBNwzTHCO0hawBDFINGZV4z7OlhRqRhSicSkW57oHQ9pLrEuWjEePegoSVs4024CcbI04inmNCxV3XxWeQf/yhCXut99xjzWaLsA4x5J2t/vueKaCvd+rmLyAb1RUDYf49l5JjuUc+L0Ne8cgRgYzwvEEUcV1acSMZR+z6Q4sgwNNx2W+FdMnGDwGkfW/+ySLoc23fDY7g8jJhkEuY7RkG+9lGNNodlNzhmMbjzRmbXW7b09d3OBnQg0d5zM/a8xk/ZOHxptakfvELUfvd6JUEObMBCKbn2XtbCGiLkFm7hAYygCIB4CSmGalVdpq1vDsDspktdBING7UrpVmIStJcrRpVVkSgSrYRKxpqO0vtpsst1/tdm2zN7CLW6gOwRqra7Vxvmj/hwbHN56fkKB/5EFmBUfy3EW57tX9rctz47kH1EFE9yRerVZ+d8DychM52lJQcvC0tvy8q5DzmhoVwqVhQ7HvH/z18aqF8H657g6evo+thFhDvg9idrnrDKDUHYfENpvc39HOEXoYCAL5yEZXeUdGGfceyPniXIt+Sq/vj/WpqQG7nZUZvgPCB8PfsZ5g473XGu2PGB0IMTjHxYPhEIbs8T6DweBMdP70DDpXfh2DZyAJx4/4n2ej3W7483BMfTt/8XtjkczE7Y0HD/NeK++N9KHqAFih9h/3XdpZr5ysS9p9L66FLfP/reiEm531aUXrvY631kTwAYSclQoYs8sJHT6tx5Nirt0BW4p56IutOVYMZZAS+LpuRYuP4TkecPYAmutNeHVLHjfx3IHextB+5VvjJa2ufh/qkeuBgAj69UDxBnqPNEY//MZjduu+vXT2M/uQ2UTv9atnQZKcF6qvbqxA4kF0QfARHZ2159Y27Y/PXaTBhANCgt5uM+L+3CoIum8OcPL2tZoszXnnsSMcK3IMBD1GRWBjg1FnVD8i/qFP7q7/zA57z1/c2LJnVzfsqeXVaqNAlB+OxNGFObv7yBIDC5c6ELMxu7Dt49fuOYD48Y595uvj9dskdv/hPW9l+X9JOEYIcoqEFart3mtWC5x4RsQJujuuqarr/8fj6EWDsb643aEThkoD4OKFjlkqVZPq3FhKgk6iGBlVRqGJv2dtcg46s1fhIHvPss/T9a/83d0bbBb/9KGvjbUBls7Dpb/rGfSCxkXIJUoYAw849ogwZ166Yn1JymsG6NhVIjJOSpLQVEQyNnkXc/Gys5w+kAVnuEKuSlx7VVWZZwYvEJRC1pP95l7izm8mDPOs3HnzSj/HDf9ifvFNn/zUWNjlmoNI3DfXG7ba26ky6FWerOpxq68Y10C1KMPdLDLrabLyeSWFpztVepnuX418XZWgj2TF8yURPLlCtpzOc2a1qqdH0SfaZwY9W9+89Loy6HcfP10H/qLMEJmGHKtGpzIy50nUmW1M0Z8ZlJd7sAyk2tXaPdsIMgM7lxBl7zkcpSTouIeTVJcEHeQb6xTvy5aVaka292zDhsJB9ix9ZvbrIIp/qJckZ0k7HgV6KUjnpaVDm1Qk7swd91iz5SQTpCuDWjwmVlJ5howiksQWGXB8O0HHN8h5SdAdO6826GMOeWphkPQjSBvOe1QlwCb5RJEZb7tiabh/NjPrPEfHGNVTcFjZgUKC6jc+KhAQnIEa9Jzt2NKs2bEGBONm7ViraWudnj1zaYMEhH31M2aLrVnDFJJ/9OXJCPqpIyDo0YMe9idLi3N8ZTrtTtDrjLkLrzqR8hFKHuzwyiu/+2rb5AH10TBtikNGfI7EJl8Xr46llNUkHDXIKrcIHkTQj5NdhkMSNRB0ZNoQdAdBvwCCzvLYKunK64J9drjTtz95ZryJH2nvzn34QWYGa4Je29Er1QfUNitfURusEYIef64Cilfx3crYxoitLJ5fI5p2t35whKS/in+Yx1aa6/VO3w79xuQE/dDt99v+9h6feR6f3UXmOsUAOcZvNBCZuhd4ThXsDtIIO4VsLAg6iDkIOu5f3BspFOdXJ8TOkIVGWX3Vsucl2ptB0KE6joAZ/SdWOHYYAMW4NWgOGceuDfw+j1aMSqAzCXrUiTBgzkCIv9lw0LdLlyYl6Ie49svrzfMqnLv8f5bn+3PLasVdrYXF3pyz1XFfe+sIsK4JOicqhe3OlilcixwBmnpB6S8uzbUZxGLrCQO3TuQhNopvjP0EQYcvB6HOjEkhoDKIZsCwkFUP+q9+azyf+FWWth66DgiIoF8HEG+kt8gN8EfvvNlOL3mJOzYJ3KhUr42Z1yzDIUlLh3Am1JWjhBNlSMykNNgrfWkbWfJ1++LZ87Q+EITDe6MXHc7wy5uenUD1FJyTEwvzdmhP2953+qQd2bOHY7JSPCn73vCTJVeIuvKzPGsN4vbMyro9s7pmTy+v2mMXLzEDhQ0BDtv+PXN2y9Je+76TRxhtPLeNEUlDO7vZp/DO2w8vcjP5d1/+6kRk6WfffQ+dFeBWC9jV5YMZkWQ2ODY8D3J4SWp+M2ARisVJgELDo4p2emA4R8H4uVeFfrFbpCtS9p+SIDLIUqiGwgGGUFOUzHIDpJx51GiRBLib585t/Z3vTYI+Jm64PyrH6299qOq1ynNG3ykxizPzgrB6w9ldQJj9bxlM8HJjfxbISZKI3XWeOBufseobmp+Th8GjNc9L57IUN27s7DfL8mdghHWfVQeePff+LryXu4i4X0aDHJ5Bn4yg/4v3fcgeXZmx1T5E4sK5SrGynBNcZdHLIsu4g6tof51pL+3W6CuuWCNbPX13UojhiKxpv1paaqTvs3a7HessdY93Jo5chRSa2ti6ZL1exx55ejKidNfRk5WwW96v3osHnQ1oX3jGB1+5zkGqEUDLeyHL1XP+eRKrJM/8PRYcM1UU0oJT7HaprmSpBemy8hkEHXauzrBEb3zc336/1j2YLF9sNn1292DA15HI894GWd2h0FCeD47l0bPPT2Tr7kiCHhl0rOic+JAZYJBmEGbXJEEpuxe+4jhgfwcg6EM45JRhjvYAz9p4ti7JZvRrstLF7x2ujRiDhfu6hRFvxe8oBcc5Z5CZJePoSXd5uHCunZDwOuPzBgObxyx027H9zYadbs9xvBtGf7IyASS+MWNH9swR35/+4mTr7viht5PYFP497W+Kw2UwyEVUXdclx/55FQVE8NAKhpCC32LAC+0/uWf7sq0r3gBWBmRTELHZ9LnTdYVXkJoUD421VQbq6yo5D5J7S9Us9V2yjQPr+8KW96Jjf2UbRx8ltrN2dAEaNH373W+OV6qd+8TLP/EBCrpWZiXK8GviW6Lqpimzw3637bZS/pxKVT1Vrq/qvEUwIwn9SAsY62WC8HjGviJsu6qSaD/Kk9j1eSVB5/FhrwRB/83JCfqRN73L9rTm3J5w3ezY+vY21eFT2yd7q/Mzs2qCehcx2SL3O6xTBA8XIzCJsWgswe50+P7w87BeQRYZsGxhfO+sYeY61sRL62u22e/ZGkvcd+zU0j4DucwpFhe2tmyt27Fut0fS3+v3rdvv0U9AtRJmfyO47Wvb16O3A9Y/h0PX+98Z9K278mcT2bq7Dx/0trusxqtC6xliL7a5uI6+POpqigx4X3WzxHti+gXHzbnq+kYfGXSvFCrDUJ7t9mCrB5PzHvZnpS6OVzGEknyIzTGg0pqzA21UUnhGPRMRvqel2+f3EV4P3+6zT4yny3QjcZ8b8VhF0G/Eq/Y6jjk3wJ+8+3Y7c2CRhjd8oEq4JssqvY8x7uSIkHIjjB5IOFEwEsudnp3b7NgTy6v2P196OTJPUCTGuJsWDQyMMB2AoatVntm3SBX5958+aYf37HHjHqXXOf4JH5+ljkmmwpyQnD+zsmZPr6zY4xeXw4nYYfb84J42qwPec/IYs/Yvb4GgD+zsZo8E/S2Hl1gO/2//6E8mMuT/6oG7HbeYG1sTdTeozIBHtJrzYuN3lAImSc+fFUEvy+HSt6j6S920NtB6QON6+binkly48FVUIYQxTlLqmXMvbaeACRzqIOh5vR3/uPYc6QOq5AIn3f8r6PbPHhovsIFjKwk6MyPJxUIUKpVZcYY5y51lsO6ajjhqIxmRIPck7aHwyvLbUFQv3Tj8Hw4yf0Ygouqxj82RDRTxPnkZRrPo7uyyvYMlvR4YwLdLgCW5BUHPAIf/EWJhxz/5nyZac//6wR+zRy5h9MrAMCOZjldVd+chm9G+9LxTyoh+OBpFaV5tSnY5vJf7v69udSp/+MqOcX296wxXcItiHJQ7O1VABtnY4cA2t1dI0B/+zmS9wG85dtr7L3n/5VgoP05mrTnese4px6mToA/C6QtyhCMnQa9GD2YBqa+pugyyFo1LZzIJFZ5XF+E6yMgelb3kmS1PwFMkLgk8jhn20ssfMU8c5eeeScZzYXPQv+hOGzAc2tdfnoygvxEl7sigk6D7GuPkjyKr78rptS4Jc2kobR/6nGxUWEGjhOQ8xqel7UyCzvXLAIP3t2epaZLsWvXcj4EElhl0D16wzDaDknDig6A7O/bSfJbho7d9MCRB3zezY/saDTs516KA3yvr6EH3Hn+c4/52k3vXP3/oixPds0cOvjUIeu0auzymiw96L73bVR+tV5e5pzghspb759pVMJiVVSRaXv7ve48LbtWjUGOsZhAtiHzlnooPq8KGhbAgMM3KobRauQ/kvdFs+Ki9XL84Duz9uwk6juPoPKpM+va5r/3+RNhdjaCneb3cPOUsmrQgu+xQvOD1EfTs+48AQAiT5X2aNqA8Rv7tyibxKhxu53UT9ENvfpe1oH0QlUEwfFudLkvSvdIkR3GlvcV+GKKFGPMYI1cz44t7EetxIadZYOQXxH+7Xa7dbNdY4JjImqBnReT5rU3a0+1YYcf3LtneOYxJ9TW/vL1NpfI+7CAmu/S6LHtHCyTWP1672et5JjgCkHUZeArWed/8Tn9gaxcns3X3HTscmiDFLsRrV4eFsi0rL159aeupOrTxhX9TEf5ItDDJwpyIVyJsoMQ9FGzSj6OfHcQ5gxG+vuDbpA5HBkSZoqj2ILbzNCDCPGf75/a4JkfDCXqGGjInk61F3pIwsF97/Dtj3a+vg4ropdeAgAj6NYD0/9NTkij93HsfsPuOH7UF9FNGyRxnX0cpb03McwRHLYriojDu/MFpPLuxac+urtpXz1+wzz3zXTqOeCc4iyDoLPfsepQQn4XI6APHj1H07C+dOmkH2+3LnAZ3AmJ2eEY0mUxxy/fMyqo9s7pqz6+uk6Rn7BFGHZn5W/Yt2rtPHCPBf2nT++Ve2eow2vuGA0t0Wj/+hckc/n98710+s51z2t2xT8e/FxlB/IR7n1HQ7D0HWfcu+pj/HD9JvOMs0oHKWaTMvrHENFR143l47+qrnIcbBMwVkH3zzexaEvTckOncFplYz0pnPsD7qr3Hy51yZEh+5uHJe9DP/e0fZ9Yy2wMARK8YRRd0nKeV2Kbyc/1YBg3qMj0nPq4+7v2yTqTL0t8k6CQ61QZa5bu5aSanzn3ZyXkgktoLsdF5LYNffxL1bCWI0XWRl68u0WqnY8d+frIxa7/8V/66ff7PLtr5rY6fKQW0Cje1zJDzE2uC7geQivKlk1ksn9KheC2DN/KxdRij9jgvd59LWr47uzXivyZr5yl4FUW/v82syh8+842xnIe0dd9/+g1Um85SXXw+SgBd7M+POsvMEzk4RVm9A3vGUuMQcMvxaLQByBJHCTtsAFaKk8HZqLLZYYsPiXpkQjpRjp3q+lUvftg59FOzGiO+MoOe13uh2bJ97bm6zB6j2lDqaB48wGdBmI7ZYLReIIP+8gsTYXf3HXfbTBB06Ip4EKAW0cMxQbgNxwgBx2pUJkrbKYYJ8TEve6/Vvn3tZjsAg3OxB/l0hSTosE2+OmqBpQgSUDpjhuMu0dOZvZ9pbwcGDHMXqacpYO4zviEOtwgF91mzQwhqYIxZCJ+tdrpeGUWntW+/9PgjE2G3tO9N1p71/v1Q+KjErrydwR3pVHOnmFSoaaOXHt8INh+Znw/NLrc1HF/ad5XnzKjzOkRpcTry+J3BSohKxvtWgRXah7R9bi4yo8+/RkQp72T/NZqHI5PHQPQAlUPG/tUMGGHf2d9Ge0rffvnh35sIu5c//AEG06/EbWuaETdI2D7uD6UGxqvYsTJ0eqWn7SbVWVlTk7W8PwPDMnNejL17rVL6yz97h4KFr6fEffHO+20HKu478Bn8nsW0AqzBzW6Xa9qFJkMMDv5FVI4gMAWCl/dRtsJxB4mxt/1hn5UoIPuV35Lrh4EitOKh9QeEfcYW223azqWFeQpODtmyganontBgIB4VGhSvm+G4XHxj4s9cy20LyDzWM9oi4YNw7eOe7br3gMAXbpJev2dPTrhP/OXbThArD3pldaPvnJUuTbFPVv5IKXwadsPL0OMZlWioB3y9jB6PITA7ZPVA+hCx7cWO7es5xUfZikKfum1oE0BWHNoAaatS1yMDavDtMcI47Uzp12EdA1dURTAZM+NVEZ969Imx7tfXchX0+OtDQAT99eF3w716ZWXFDhw4YJ8AQT921ODscQo6ZrRWPapVDJ3bsk9Jz35g39ZhgNzxmOXYL5DlR89fsN9+9rss606CjhIl9h8VBB1G4YGbjtux+Xl7z4mb7UB7rsriZ7Ayy3ZylrATujoa/dzqCkesvbi+bs+urDmNhPhcEPSTS3vtnTcdpUL42Y2ObfaHJDfIANy6b5GO/89+6Su2vLxs+/fvv6brmNj9/be8wUdtsVfSs7wURIJAVGTJIcGRpDxLpstxa26662/vIfTtv/7puwHFUphlicx4sNEsDU7MssogRYPQS+nl3k5kqyh5ENfMNOdxVCHYIOTu8LiL5oKAUFwf2CceeWws3PD6xO47H/1rTlziejKrGVjxZ+VkpRp2BDn8YDxgkOs0Ky4iNpwEHefrwYwrEPQs4Y+Ntu43jyLvAP8ygh5OdkrS0B8Igg4y4AGtHF+3O9Tiywsb8Rv+86+OhV3i9ukP/oh94ex5rmEXRBztjUtiWRfjlQQ91lFmkWKd5XX3P+8m9HFL7PaQK+4d71nkF2rn9UpuZ9KB+mc+61UqQD1IN8Bc2L79r+eemAi79546w7JgloRH1hHCbsycx/kgQwNinDEPlrSzPN2/2YfJGeXIRnjmBzYA79krMpoMArGHF3OsXYUaJZp4jxThwj20WzMBr/ORYz7BAcQrv/z96qAHJxsgqxrtM1SWJ0F37Q+QXYzCSrKCY/zm+e9NhN2bztxlM40WFxvah0D0WtnvHH0hJOjo8S6ce/j+cJ3pPqMnPUrcXVvCy7BpP4tKIw9uxB0W5xsFPHW7TmbxQ6AOop9u15yEp9Of+0a9d/nqJEEHThhrZGbzMzN2gArmQ9vsdCkeuIJsYyiao+T2vz796ETY7V28I+agh4ZAVAmwLBjTTKgxEEGPWFNOpI2B85qgL1TEPNt7ttBru7XNCjGUmsOWJ0FPJz81DRpFBj0DSBns4b0XbNSnC/h18f2+tgtZieCZOj9GD9S7+jv60oE1yqihk7LUQtCjZ7/2yB9MhN3Tf+N9VLK/GkEvLUxq51wrQb88fDhqr+j97PrgmqAnJqNVbGH+HaaCrNcZ+2tyMYj7crdnt/3W/x4Lt3KPXThzrw1RQUaC7mtvgaJw6PeGLfVMuhP0kOHleLMdQ56VQTZWB7rvAJLNdUciDcLowT+v3KiDXxmEAkFHGxgEHLG+IVgH+3RgccHmmi3rYhJBTLLBZ4D442DbYT8ubm9ybaM1A1MDUFWDACTeD33VXrmxTUK5AYKO444FCYL+rWe+MRZ2ucc+eNvNTtArcu0aGiME3Tf+aroQfy1Je0XQs+0ueuPRzoP9JYKN/jauPQKROCa1IjNTxd3jPswqo6xSQlYceMB3B0FPXznHb+K6YBIQ7PVSy4WFWZlUJF7w+bA/IPBuC7A3Duy/fOOpsbC7tlWtZ02KgAj6pMjdoK978cUX7fTp0zfo0V//w37hhRfs1KlT1/TGwq6GaRzc8CphNxl2wm301hxn3Qk7YXdNhv0anqR1dw0gXeUpwm4y7MbBTXusbN1kq+zyV4277q7X5+p9LkdABP3P2apAKdFLL71kS0tLoyWyf85wQER4bW3NTpw4Uc3jfi0IhJ2XXI2LG3AVdpNhJ9z8rpxk3Qk7YfdaNv21Hte6ey2Erv64sJsMu0lw0x4rWzfZaqtfNem6e72fq9dfHQERdK0OISAEhIAQEAJCQAgIASEgBISAEBACU4CACPoUXAQdghAQAkJACAgBISAEhIAQEAJCQAgIARF0rQEhIASEgBAQAkJACAgBISAEhIAQEAJTgIAI+hRcBB2CEBACQkAICAEhIASEgBAQAkJACAgBEXStASEgBISAEBACQkAICAEhIASEgBAQAlOAgAj6FFwEHYIQEAJCQAgIASEgBISAEBACQkAICAERdK0BISAEhIAQEAJCQAgIASEgBISAEBACU4CACPoUXAQdghAQAkJACAgBISAEhIAQEAJCQAgIARF0rQEhIASEgBAQAkJACAgBISAEhIAQEAJTgIAI+hRcBB2CEBACQkAICAEhIASEgBAQAkJACAgBEXStASEgBISAEBACQkAICAEhIASEgBAQAlOAgAj6FFwEHYIQEAJCQAgIASEgBISAEBACQkAICAERdK0BISAEhIAQEAJCQAgIASEgBISAEBACU4CACPoUXAQdghAQAkJACAgBISAEhIAQEAJCQAgIARF0rQEhIASEgBAQAkJACAgBISAEhIAQEAJTgIAI+hRcBB2CEBACQkAICAEhIASEgBAQAkJACAgBEXStASEgBISAEBACQkAICAEhIASEgBAQAlOAgAj6FFwEHYIQEAJCQAgIASEgBISAEBACQkAICAERdK0BISAEhIAQEAJCQAgIASEgBISAEBACU4CACPoUXAQdghAQAkJACAgBISAEhIAQEAJCQAgIARF0rQEhIASEgBAQAkJACAgBISAEhIAQEAJTgIAI+hRcBB2CEBACQkAICAEhIASEgBAQAkJACAgBEXStASEgBISAEBACQkAICAEhIASEgBAQAlOAgAj6FFwEHYIQEAJCQAgIASEgBISAEBACQkAICAERdK0BISAEhIAQEAJCQAgIASEgBISAEBACU4CACPoUXAQdghAQAkJACAgBISAEhIAQEAJCQAgIARF0rQEhIASEgBAQAkJACAgBISAEhIAQEAJTgIAI+hRcBB2CEBACQkAICAEhIASEgBAQAkJACAgBEXStASEgBISAEBACQkAICAEhIASEgBAQAlOAgAj6FFwEHYIQEAJCQAgIASEgBISAEBACQkAICAERdK0BISAEhIAQEAJCQAgIASEgBISAEBACU4CACPoUXAQdghAQAkJACAgBISAEhIAQEAJCQAgIARF0rQEhIASEgBAQAkJACAgBISAEhIAQEAJTgIAI+hRcBB2CEBACQkAICAEhIASEgBAQAkJACAgBEXStASEgBISAEBACQkAICAEhIASEgBAQAlOAgAj6FFwEHYIQEAJCQAgIASEgBISAEBACQkAICAERdK0BISAEhIAQEAJCQAgIASEgBISAEBACU4CACPoUXAQdghAQAkJACAgBISAEhIAQEAJCQAgIARF0rQEhIASEgBAQAkJACAgBISAEhIAQEAJTgIAI+hRcBB2CEBACQkAICAEhIASEgBAQAkJACAgBEXStASEgBISAEBACQkAICAEhIASEgBAQAlOAgAj6FFwEHYIQEAJCQAgIASEgBISAEBACQkAICAERdK0BISAEhIAQEAJCQAgIASEgBISAEBACU4CACPoUXAQdghAQAkJACAgBISAEhIAQEAJCQAgIARF0rQEhIASEgBAQAkJACAgBISAEhIAQEAJTgIAI+hRcBB2CEBACQkAICAEhIASEgBAQAkJACAgBEXStASEgBISAEBACQkAICAEhIASEgBAQAlOAgAj6FFwEHYIQEAJCQAgIASEgBISAEBACQkAICAERdK0BISAEhIAQEAJCQAgIASEgBISAEBACU4CACPoUXAQdgi6IahIAAACLSURBVBAQAkJACAgBISAEhIAQEAJCQAgIARF0rQEhIASEgBAQAkJACAgBISAEhIAQEAJTgIAI+hRcBB2CEBACQkAICAEhIASEgBAQAkJACAgBEXStASEgBISAEBACQkAICAEhIASEgBAQAlOAgAj6FFwEHYIQEAJCQAgIASEgBISAEBACQkAICIH/A6dI+8TVLP0MAAAAAElFTkSuQmCC\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_class_data(conf_train, sup_train, no_col_train, y_train, class_ind=0, n_cols=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1d069248",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "\n",
    "conf_save = [(conf_train, y_train, conf_train_colours), (conf_val, y_val, conf_val_colours), (conf_test, y_test, conf_test_colours)]\n",
    "sup_save = [(sup_train, y_train, sup_train_colours), (sup_val, y_val, sup_val_colours), (sup_test, y_test, sup_test_colours)]\n",
    "no_col_save = [(no_col_train, y_train), (no_col_val, y_val), (no_col_test, y_test)]\n",
    "\n",
    "\n",
    "with open('mnist_conf_data.pkl', 'wb') as f:\n",
    "    pkl.dump(conf_save, f)\n",
    "    \n",
    "with open('mnist_sup_data.pkl', 'wb') as f:\n",
    "    pkl.dump(sup_save, f)\n",
    "    \n",
    "with open('mnist_no_col_data.pkl', 'wb') as f:\n",
    "    pkl.dump(no_col_save, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6452747f",
   "metadata": {},
   "source": [
    "## Model Training\n",
    "Following the implementation of Anders et. al (2022): https://www.sciencedirect.com/science/article/pii/S1566253521001573\n",
    "and https://colab.research.google.com/github/reiinakano/invariant-risk-minimization/blob/master/invariant_risk_minimization_colored_mnist.ipynb#scrollTo=9hYJRewnv80x\n",
    "\n",
    "\n",
    "All models on colored MNIST in Sections 3.2 and 4.1 are trained\n",
    "using the AdaDelta algorithm with a learning rate of 1.0, which is\n",
    "multiplied by 0.7 after each epoch, for 10 epochs. The a posteriori ClArC\n",
    "is trained for 10 epochs on top of the baseline model, which has also\n",
    "been trained for 10 epochs. The network consists of 2 convolutional\n",
    "layers, followed by a max-pooling, and finally 2 fully-connected layers.\n",
    "Dropout is used after the max pooling and after the first fully-connected\n",
    "layer, with 25 percent and 50 percent dropout probabilities respectively. ReLU activations follow all fully-connected layers except the\n",
    "final one. The model used for 5.1 is trained with SGD, with a learning\n",
    "rate of 0.001 for 5 epochs. The architecture, however, is the same as\n",
    "for the other colored MNIST models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b5631e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 20, 5, 1)\n",
    "        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n",
    "        self.fc1 = nn.Linear(5 * 5 * 50, 1250)\n",
    "        self.fc2 = nn.Linear(1250, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        x = F.relu(self.conv1(x))\n",
    "\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "\n",
    "        x = F.relu(self.conv2(x))\n",
    "\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "\n",
    "        x = x.view(-1, 5 * 5 * 50)\n",
    "\n",
    "        x = F.relu(self.fc1(x))\n",
    "\n",
    "        logits = self.fc2(x)\n",
    "\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a3a5d77d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(lr,epochs,train_loader,val_loader,name_model,momentum=0.9,weight_decay=1e-3,num_models=10):\n",
    "    metric = AUROC(task='multiclass', num_classes=10) \n",
    "    softmax = nn.Softmax(dim=1)\n",
    "    \n",
    "    training_accuracy = Accuracy(task='multiclass', num_classes=10).to(DEVICE)\n",
    "    val_accuracy = Accuracy(task='multiclass', num_classes=10).to(DEVICE)\n",
    "\n",
    "    torch.manual_seed(SEED)\n",
    "\n",
    "    trained_models = []\n",
    "    training_logs = []\n",
    "\n",
    "    for training_ind in range(num_models):\n",
    "        cnn_conf=Net()\n",
    "        cnn_conf.to(DEVICE)\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "        \n",
    "        # Anders et. al uses mainly AdaDelta but also SGD in one section\n",
    "#         optimizer = optim.SGD(cnn_conf.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)\n",
    "        optimizer = optim.Adadelta(cnn_conf.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "\n",
    "\n",
    "        loss_list_train=[]\n",
    "        loss_list_val=[]\n",
    "        acc_list_train=[]\n",
    "        acc_list_val=[]\n",
    "        \n",
    "\n",
    "        min_loss = 100000000\n",
    "    #     last_loss = 1000000000\n",
    "\n",
    "        for epoch in range(epochs):  # loop over the dataset multiple times\n",
    "            print(f' epoch {epoch+1} in {epochs}')\n",
    "            t0=time.time()\n",
    "            epoch_loss = 0.0\n",
    "            epoch_loss_val= 0.0\n",
    "            batch_acc=[]\n",
    "            batch_acc_val=[]\n",
    "\n",
    "            auroc_train=[]\n",
    "            auroc_val=[]\n",
    "\n",
    "            pred_train=[]\n",
    "            pred_val=[]\n",
    "            true_labels_train=[]\n",
    "            true_labels_val=[]\n",
    "\n",
    "            for i, data in enumerate(train_loader):\n",
    "                inputs, labels = data\n",
    "\n",
    "                inputs = inputs.to(DEVICE,dtype=torch.float)\n",
    "                labels = labels.type(torch.LongTensor)\n",
    "                labels=labels.to(DEVICE)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward + backward + optimize\n",
    "                outputs = cnn_conf(inputs).squeeze()\n",
    "                _, predicted = torch.max(outputs.data,1)\n",
    "                loss = criterion(outputs, labels)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "                epoch_loss += loss.item()        \n",
    "                batch_acc.append(training_accuracy(predicted, labels).item())\n",
    "\n",
    "#                 print(softmax(outputs)[:,1].detach().numpy().shape)\n",
    "#                 pred_train=np.concatenate((pred_train, softmax(outputs)[:,1].detach().numpy()))\n",
    "#                 true_labels_train=np.concatenate((true_labels_train, labels.cpu().numpy()))\n",
    "                \n",
    "                auroc_train.append(metric(outputs,labels.to(torch.int32)))\n",
    "\n",
    "            auroc_train = sum(auroc_train)/len(auroc_train)\n",
    "\n",
    "            for i_v, data_val in enumerate(val_loader):\n",
    "                inputs_val, labels_val = data_val\n",
    "\n",
    "                inputs_val = inputs_val.to(DEVICE,dtype=torch.float)\n",
    "                labels_val = labels_val.type(torch.LongTensor)\n",
    "                labels_val=labels_val.to(DEVICE)\n",
    "\n",
    "                outputs_val = cnn_conf(inputs_val).squeeze()\n",
    "                _, predicted_val = torch.max(outputs_val.data, 1)\n",
    "\n",
    "                loss_val = criterion(outputs_val, labels_val)\n",
    "\n",
    "                epoch_loss_val += loss_val.item()        \n",
    "                batch_acc_val.append(val_accuracy(predicted_val, labels_val).item())\n",
    "\n",
    "                auroc_val.append(metric(outputs_val,labels_val.to(torch.int32)))\n",
    "\n",
    "#                 pred_val=np.concatenate((pred_val, softmax(outputs_val)[:,1].cpu().detach().numpy()))\n",
    "#                 true_labels_val=np.concatenate((true_labels_val, labels_val.cpu().detach().numpy()))\n",
    "            auroc_val = sum(auroc_val)/len(auroc_val)\n",
    "\n",
    "            if epoch_loss_val < min_loss:\n",
    "                min_loss = epoch_loss_val\n",
    "                save_model = type(cnn_conf)() # get a new instance\n",
    "                save_model.load_state_dict(cnn_conf.state_dict()) # copy weights and stuff\n",
    "\n",
    "                torch.save(save_model.state_dict(), f'{str(name_model)}_{training_ind}.pt')\n",
    "\n",
    "#             auroc_val=metric(torch.tensor(pred_val),torch.tensor(true_labels_val).to(torch.int32))\n",
    "\n",
    "            epoch_acc=sum(batch_acc)/len(batch_acc)\n",
    "            epoch_acc_val=sum(batch_acc_val)/len(batch_acc_val)\n",
    "\n",
    "            print(f'epoch train loss: {epoch_loss} | epoch train acc {epoch_acc} | AUROC: {auroc_train}')\n",
    "            print(f'epoch val loss: {epoch_loss_val} | epoch val acc {epoch_acc_val} | AUROC: {auroc_val}')\n",
    "            print(f'time elapsed: {round(time.time()-t0,2)} s')\n",
    "\n",
    "            loss_list_train.append(epoch_loss)\n",
    "            loss_list_val.append(epoch_loss_val)\n",
    "            acc_list_train.append(epoch_acc)\n",
    "            acc_list_val.append(epoch_acc_val)\n",
    "\n",
    "            epoch_loss = 0.0\n",
    "            epoch_loss_val=0.0\n",
    "\n",
    "        print('Finished Training')\n",
    "        print()\n",
    "        print('\\t \\t *******************')\n",
    "        print()\n",
    "        \n",
    "        torch.save(save_model.state_dict(), f'{str(name_model)}_{training_ind}.pt')\n",
    "\n",
    "        trained_models.append(save_model)\n",
    "        training_logs.append([acc_list_train, loss_list_train, acc_list_val, loss_list_val])\n",
    "#     plt.plot(loss_list_train)\n",
    "#     plt.plot(loss_list_val)\n",
    "#     plt.plot([i*100 for i in acc_list_train])\n",
    "#     plt.plot([i*100 for i in acc_list_va])\n",
    "    return trained_models, [acc_list_train, loss_list_train, acc_list_val, loss_list_val]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9415ca55",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(TensorDataset(torch.tensor(conf_train.transpose(0,3,1,2)), torch.tensor(y_train)), batch_size=1000, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(torch.tensor(conf_val.transpose(0,3,1,2)), torch.tensor(y_val)), batch_size=1000, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4d40a193",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.005\n",
    "epochs = 100\n",
    "num_models=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1857af32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " epoch 1 in 20\n",
      "epoch train loss: 50.64732766151428 | epoch train acc 0.105530303648927 | AUROC: 0.5316533446311951\n",
      "epoch val loss: 18.41024112701416 | epoch val acc 0.10900000017136335 | AUROC: 0.5564974546432495\n",
      "time elapsed: 11.64 s\n",
      " epoch 2 in 20\n",
      "epoch train loss: 50.611862897872925 | epoch train acc 0.11254545504396612 | AUROC: 0.5752776861190796\n",
      "epoch val loss: 18.398555517196655 | epoch val acc 0.11499999929219484 | AUROC: 0.5979584455490112\n",
      "time elapsed: 18.14 s\n",
      " epoch 3 in 20\n",
      "epoch train loss: 50.57416248321533 | epoch train acc 0.1163333339447325 | AUROC: 0.6149024367332458\n",
      "epoch val loss: 18.381505966186523 | epoch val acc 0.11687499936670065 | AUROC: 0.6369352340698242\n",
      "time elapsed: 12.57 s\n",
      " epoch 4 in 20\n",
      "epoch train loss: 50.53849387168884 | epoch train acc 0.12028787962415001 | AUROC: 0.6452692747116089\n",
      "epoch val loss: 18.36642289161682 | epoch val acc 0.12512499932199717 | AUROC: 0.659362256526947\n",
      "time elapsed: 13.63 s\n",
      " epoch 5 in 20\n",
      "epoch train loss: 50.501516342163086 | epoch train acc 0.1265151524408297 | AUROC: 0.667536199092865\n",
      "epoch val loss: 18.35441303253174 | epoch val acc 0.13062500022351742 | AUROC: 0.6761701107025146\n",
      "time elapsed: 14.98 s\n",
      " epoch 6 in 20\n",
      "epoch train loss: 50.46385669708252 | epoch train acc 0.13966666703874414 | AUROC: 0.6831632256507874\n",
      "epoch val loss: 18.343544483184814 | epoch val acc 0.14875000156462193 | AUROC: 0.6871853470802307\n",
      "time elapsed: 15.53 s\n",
      " epoch 7 in 20\n",
      "epoch train loss: 50.42383599281311 | epoch train acc 0.16678787767887115 | AUROC: 0.6928735971450806\n",
      "epoch val loss: 18.32955574989319 | epoch val acc 0.17024999856948853 | AUROC: 0.6962041854858398\n",
      "time elapsed: 16.57 s\n",
      " epoch 8 in 20\n",
      "epoch train loss: 50.38061261177063 | epoch train acc 0.18118181960149246 | AUROC: 0.7007434964179993\n",
      "epoch val loss: 18.31107187271118 | epoch val acc 0.1906249988824129 | AUROC: 0.7020617723464966\n",
      "time elapsed: 15.17 s\n",
      " epoch 9 in 20\n",
      "epoch train loss: 50.333781242370605 | epoch train acc 0.19112121246077798 | AUROC: 0.7061882019042969\n",
      "epoch val loss: 18.293110847473145 | epoch val acc 0.19099999964237213 | AUROC: 0.7060748934745789\n",
      "time elapsed: 20.69 s\n",
      " epoch 10 in 20\n",
      "epoch train loss: 50.281246185302734 | epoch train acc 0.1997575732794675 | AUROC: 0.7097887396812439\n",
      "epoch val loss: 18.275744438171387 | epoch val acc 0.2058749981224537 | AUROC: 0.7093531489372253\n",
      "time elapsed: 21.19 s\n",
      " epoch 11 in 20\n",
      "epoch train loss: 50.22543716430664 | epoch train acc 0.20689393783157523 | AUROC: 0.7123419046401978\n",
      "epoch val loss: 18.252445697784424 | epoch val acc 0.20412499830126762 | AUROC: 0.7120485305786133\n",
      "time elapsed: 15.6 s\n",
      " epoch 12 in 20\n",
      "epoch train loss: 50.161032915115356 | epoch train acc 0.21143939413807608 | AUROC: 0.7140297293663025\n",
      "epoch val loss: 18.22587513923645 | epoch val acc 0.2166249994188547 | AUROC: 0.7145392894744873\n",
      "time elapsed: 15.05 s\n",
      " epoch 13 in 20\n",
      "epoch train loss: 50.089587688446045 | epoch train acc 0.21678787876259198 | AUROC: 0.715077817440033\n",
      "epoch val loss: 18.20102024078369 | epoch val acc 0.21749999932944775 | AUROC: 0.714411199092865\n",
      "time elapsed: 15.09 s\n",
      " epoch 14 in 20\n",
      "epoch train loss: 50.01269173622131 | epoch train acc 0.2223333330316977 | AUROC: 0.7146503925323486\n",
      "epoch val loss: 18.168320894241333 | epoch val acc 0.2251249961555004 | AUROC: 0.7133476138114929\n",
      "time elapsed: 15.15 s\n",
      " epoch 15 in 20\n",
      "epoch train loss: 49.921823263168335 | epoch train acc 0.22453030266545035 | AUROC: 0.7148599028587341\n",
      "epoch val loss: 18.139012813568115 | epoch val acc 0.22174999862909317 | AUROC: 0.7081193327903748\n",
      "time elapsed: 21.18 s\n",
      " epoch 16 in 20\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\1\\ipykernel_19636\\1339579275.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mval_loader\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'mnist_conf'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmomentum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mweight_decay\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1e-3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnum_models\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_models\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\1\\ipykernel_19636\\377032434.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(lr, epochs, train_loader, val_loader, name_model, momentum, weight_decay, num_models)\u001b[0m\n\u001b[0;32m     60\u001b[0m                 \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m                 \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 62\u001b[1;33m                 \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m                 \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    487\u001b[0m             )\n\u001b[0;32m    488\u001b[0m         torch.autograd.backward(\n\u001b[1;32m--> 489\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    490\u001b[0m         )\n\u001b[0;32m    491\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\autograd\\__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    197\u001b[0m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0;32m    198\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m         allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m def grad(\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train(lr,epochs,train_loader,val_loader,'mnist_conf',momentum=0.9,weight_decay=1e-3,num_models=num_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59fffd1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " epoch 1 in 100\n",
      "epoch train loss: 39.138875007629395 | epoch train acc 0.1007647058543037 | AUROC: 0.526954710483551\n",
      "epoch val loss: 13.813894510269165 | epoch val acc 0.09866666793823242 | AUROC: 0.5267477035522461\n",
      "time elapsed: 9.52 s\n",
      " epoch 2 in 100\n",
      "epoch train loss: 39.13716530799866 | epoch train acc 0.1003529411904952 | AUROC: 0.5314996838569641\n",
      "epoch val loss: 13.81257438659668 | epoch val acc 0.10075000176827113 | AUROC: 0.5348599553108215\n",
      "time elapsed: 11.28 s\n",
      " epoch 3 in 100\n",
      "epoch train loss: 39.13586187362671 | epoch train acc 0.10182352933813543 | AUROC: 0.5403558015823364\n",
      "epoch val loss: 13.811071634292603 | epoch val acc 0.10116666679581006 | AUROC: 0.5439146161079407\n",
      "time elapsed: 14.16 s\n",
      " epoch 4 in 100\n",
      "epoch train loss: 39.13241100311279 | epoch train acc 0.10170588204089333 | AUROC: 0.5471667051315308\n",
      "epoch val loss: 13.809785604476929 | epoch val acc 0.10483333344260852 | AUROC: 0.5506991147994995\n",
      "time elapsed: 15.42 s\n",
      " epoch 5 in 100\n",
      "epoch train loss: 39.12935400009155 | epoch train acc 0.10288235326023663 | AUROC: 0.5513352751731873\n",
      "epoch val loss: 13.810276508331299 | epoch val acc 0.10358333215117455 | AUROC: 0.5556597113609314\n",
      "time elapsed: 12.48 s\n",
      " epoch 6 in 100\n",
      "epoch train loss: 39.12807750701904 | epoch train acc 0.10535294124308754 | AUROC: 0.5592353343963623\n",
      "epoch val loss: 13.809004068374634 | epoch val acc 0.10633333399891853 | AUROC: 0.5614526271820068\n",
      "time elapsed: 11.21 s\n",
      " epoch 7 in 100\n",
      "epoch train loss: 39.12401008605957 | epoch train acc 0.10664705800659516 | AUROC: 0.5650124549865723\n",
      "epoch val loss: 13.807663202285767 | epoch val acc 0.11000000188748042 | AUROC: 0.5666653513908386\n",
      "time elapsed: 11.12 s\n",
      " epoch 8 in 100\n",
      "epoch train loss: 39.12191867828369 | epoch train acc 0.10564705904792338 | AUROC: 0.57058185338974\n",
      "epoch val loss: 13.80726957321167 | epoch val acc 0.10791666805744171 | AUROC: 0.5740278363227844\n",
      "time elapsed: 11.3 s\n",
      " epoch 9 in 100\n",
      "epoch train loss: 39.11876559257507 | epoch train acc 0.10735294091350892 | AUROC: 0.5762240886688232\n",
      "epoch val loss: 13.806779384613037 | epoch val acc 0.1096666653951009 | AUROC: 0.5782896280288696\n",
      "time elapsed: 12.58 s\n",
      " epoch 10 in 100\n",
      "epoch train loss: 39.11588406562805 | epoch train acc 0.1087647054125281 | AUROC: 0.5837511420249939\n",
      "epoch val loss: 13.805230379104614 | epoch val acc 0.10908333336313565 | AUROC: 0.5849148035049438\n",
      "time elapsed: 12.86 s\n",
      " epoch 11 in 100\n",
      "epoch train loss: 39.115212202072144 | epoch train acc 0.10870588176390704 | AUROC: 0.5864524245262146\n",
      "epoch val loss: 13.804805755615234 | epoch val acc 0.10925000036756198 | AUROC: 0.5890591740608215\n",
      "time elapsed: 12.39 s\n",
      " epoch 12 in 100\n",
      "epoch train loss: 39.11161684989929 | epoch train acc 0.10705882354694254 | AUROC: 0.5923946499824524\n",
      "epoch val loss: 13.80430817604065 | epoch val acc 0.10883333409825961 | AUROC: 0.5905924439430237\n",
      "time elapsed: 11.35 s\n",
      " epoch 13 in 100\n",
      "epoch train loss: 39.1075279712677 | epoch train acc 0.10582352955551709 | AUROC: 0.5980277061462402\n",
      "epoch val loss: 13.80246615409851 | epoch val acc 0.11666666716337204 | AUROC: 0.5966504216194153\n",
      "time elapsed: 11.27 s\n",
      " epoch 14 in 100\n",
      "epoch train loss: 39.10660195350647 | epoch train acc 0.1076470591566142 | AUROC: 0.6019915342330933\n",
      "epoch val loss: 13.801581859588623 | epoch val acc 0.11816666647791862 | AUROC: 0.6039619445800781\n",
      "time elapsed: 11.83 s\n",
      " epoch 15 in 100\n",
      "epoch train loss: 39.10271167755127 | epoch train acc 0.11276470694471807 | AUROC: 0.6077223420143127\n",
      "epoch val loss: 13.799953937530518 | epoch val acc 0.12350000068545341 | AUROC: 0.6085469126701355\n",
      "time elapsed: 12.67 s\n",
      " epoch 16 in 100\n",
      "epoch train loss: 39.09918212890625 | epoch train acc 0.11888235369149376 | AUROC: 0.6103746294975281\n",
      "epoch val loss: 13.799769878387451 | epoch val acc 0.12374999995032947 | AUROC: 0.6118297576904297\n",
      "time elapsed: 11.39 s\n",
      " epoch 17 in 100\n",
      "epoch train loss: 39.09735298156738 | epoch train acc 0.12023529498016133 | AUROC: 0.6149008274078369\n",
      "epoch val loss: 13.799004077911377 | epoch val acc 0.1266666700442632 | AUROC: 0.6164283156394958\n",
      "time elapsed: 12.1 s\n",
      " epoch 18 in 100\n",
      "epoch train loss: 39.09290266036987 | epoch train acc 0.12299999945303973 | AUROC: 0.619792640209198\n",
      "epoch val loss: 13.79741621017456 | epoch val acc 0.13558333242932954 | AUROC: 0.6179998517036438\n",
      "time elapsed: 14.49 s\n",
      " epoch 19 in 100\n",
      "epoch train loss: 39.090803384780884 | epoch train acc 0.12894117744529948 | AUROC: 0.6244782209396362\n",
      "epoch val loss: 13.796401977539062 | epoch val acc 0.13925000155965486 | AUROC: 0.6233723163604736\n",
      "time elapsed: 13.06 s\n",
      " epoch 20 in 100\n",
      "epoch train loss: 39.08686113357544 | epoch train acc 0.1389411771122147 | AUROC: 0.626621663570404\n",
      "epoch val loss: 13.795604467391968 | epoch val acc 0.14091666539510092 | AUROC: 0.6234089136123657\n",
      "time elapsed: 12.49 s\n",
      " epoch 21 in 100\n",
      "epoch train loss: 39.08323574066162 | epoch train acc 0.14705882528248956 | AUROC: 0.6314849257469177\n",
      "epoch val loss: 13.794787645339966 | epoch val acc 0.1431666687130928 | AUROC: 0.6295976042747498\n",
      "time elapsed: 11.63 s\n",
      " epoch 22 in 100\n",
      "epoch train loss: 39.0815749168396 | epoch train acc 0.15005882434985218 | AUROC: 0.6320582628250122\n",
      "epoch val loss: 13.793959856033325 | epoch val acc 0.14975000297029814 | AUROC: 0.6306307315826416\n",
      "time elapsed: 12.42 s\n",
      " epoch 23 in 100\n",
      "epoch train loss: 39.07825803756714 | epoch train acc 0.15347058808102326 | AUROC: 0.634202778339386\n",
      "epoch val loss: 13.792433500289917 | epoch val acc 0.15275000035762787 | AUROC: 0.6328229904174805\n",
      "time elapsed: 12.81 s\n",
      " epoch 24 in 100\n",
      "epoch train loss: 39.07628870010376 | epoch train acc 0.15570588234592886 | AUROC: 0.6372771263122559\n",
      "epoch val loss: 13.790441513061523 | epoch val acc 0.15716666479905447 | AUROC: 0.6389650106430054\n",
      "time elapsed: 13.59 s\n",
      " epoch 25 in 100\n"
     ]
    }
   ],
   "source": [
    "train_loader = DataLoader(TensorDataset(torch.tensor(sup_train.transpose(0,3,1,2)), torch.tensor(y_train)), batch_size=1000, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(torch.tensor(sup_val.transpose(0,3,1,2)), torch.tensor(y_val)), batch_size=1000, shuffle=True)\n",
    "\n",
    "train(lr,epochs,train_loader,val_loader,'mnist_sup',momentum=0.9,weight_decay=1e-3,num_models=num_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7bf2bac",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(TensorDataset(torch.tensor(no_col_train.transpose(0,3,1,2)), torch.tensor(y_train)), batch_size=1000, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(torch.tensor(no_col_val.transpose(0,3,1,2)), torch.tensor(y_val)), batch_size=1000, shuffle=True)\n",
    "\n",
    "train(lr,epochs,train_loader,val_loader,'mnist_no_col',momentum=0.9,weight_decay=1e-3,num_models=num_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "12cef5c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_trained(path, device='cpu'):\n",
    "    model = Net()\n",
    "    model.load_state_dict(torch.load(path,map_location=device))\n",
    "    return model\n",
    "\n",
    "def print_AUC(loader, model_ind=0, device='cpu'):\n",
    "\n",
    "    cnn_conf=load_trained(f'./mnist_conf_{model_ind}.pt').to(device)\n",
    "    cnn_sup=load_trained(f'./mnist_sup_{model_ind}.pt').to(device)\n",
    "    cnn_no=load_trained(f'./mnist_no_col_{model_ind}.pt').to(device)\n",
    "    \n",
    "    #models -> cnn_no; cnn_sup; cnn_conf\n",
    "\n",
    "    softmax = nn.Softmax(dim=1)\n",
    "    metric = AUROC(num_classes=10, task='multiclass')\n",
    "\n",
    "    model_conf_test_conf_pred=np.array([])\n",
    "    model_sup_test_conf_pred=np.array([])\n",
    "    model_no_test_conf_pred=np.array([])\n",
    "    true_labels=np.array([])\n",
    "    \n",
    "    model_conf_auroc_test = []\n",
    "    model_sup_auroc_test = []\n",
    "    model_no_col_auroc_test = []\n",
    "\n",
    "    for i_v, data_val in enumerate(loader):\n",
    "        inputs_val, labels_val = data_val\n",
    "        inputs_val = inputs_val.to(DEVICE,dtype=torch.float)\n",
    "        labels_val = labels_val.type(torch.LongTensor)\n",
    "        labels_val=labels_val.to(DEVICE)\n",
    "\n",
    "        outputs_conf = cnn_conf(inputs_val).squeeze()\n",
    "        outputs_sup = cnn_sup(inputs_val).squeeze()\n",
    "        outputs_no = cnn_no(inputs_val).squeeze()\n",
    "\n",
    "        out_pred_conf=softmax(outputs_conf)\n",
    "        out_pred_sup=softmax(outputs_sup)\n",
    "        out_pred_no=softmax(outputs_no)\n",
    "\n",
    "#         model_conf_test_conf_pred=np.concatenate((model_conf_test_conf_pred, out_pred_conf[:,1].cpu().detach().numpy()))\n",
    "#         model_sup_test_conf_pred=np.concatenate((model_sup_test_conf_pred, out_pred_sup[:,1].cpu().detach().numpy()))\n",
    "#         model_no_test_conf_pred=np.concatenate((model_no_test_conf_pred, out_pred_no[:,1].cpu().detach().numpy()))\n",
    "\n",
    "#         print(labels_val.shape)\n",
    "#         print(model_conf_test_conf_pred)\n",
    "\n",
    "        model_conf_auroc_test.append(metric(out_pred_conf, labels_val))\n",
    "        model_sup_auroc_test.append(metric(out_pred_sup, labels_val))\n",
    "        model_no_col_auroc_test.append(metric(out_pred_no, labels_val))\n",
    "        \n",
    "#         true_labels=np.concatenate((true_labels, labels_val.cpu().numpy()))\n",
    "\n",
    "    print('model: confounder', sum(model_conf_auroc_test)/len(model_conf_auroc_test))\n",
    "    print('model: suppressor',sum(model_sup_auroc_test)/len(model_sup_auroc_test))\n",
    "    print('model: no colour',sum(model_no_col_auroc_test)/len(model_no_col_auroc_test))\n",
    "    print()\n",
    "\n",
    "    l=[sum(model_conf_auroc_test)/len(model_conf_auroc_test),\n",
    "       sum(model_sup_auroc_test)/len(model_sup_auroc_test),\n",
    "       sum(model_no_col_auroc_test)/len(model_no_col_auroc_test)]\n",
    "    return l\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "baad3499",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "with open('mnist_conf_data.pkl', 'rb') as f:\n",
    "    conf_data = pkl.load(f)\n",
    "    \n",
    "with open('mnist_sup_data.pkl', 'rb') as f:\n",
    "    sup_data = pkl.load(f)\n",
    "    \n",
    "with open('mnist_no_col_data.pkl', 'rb') as f:\n",
    "    no_col_data = pkl.load(f)\n",
    "\n",
    "batch_size = 64\n",
    "\n",
    "confounder_test_loader = DataLoader(TensorDataset(torch.tensor(conf_data[2][0].transpose(0,3,1,2)), torch.tensor(conf_data[2][1])),batch_size=batch_size, shuffle=True)\n",
    "suppressor_test_loader = DataLoader(TensorDataset(torch.tensor(sup_data[2][0].transpose(0,3,1,2)), torch.tensor(sup_data[2][1])),batch_size=batch_size, shuffle=True)\n",
    "no_col_test_loader = DataLoader(TensorDataset(torch.tensor(no_col_data[2][0].transpose(0,3,1,2)), torch.tensor(no_col_data[2][1])),batch_size=batch_size, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9c4b2f38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL 0\n",
      "confounder data:\n",
      "model: confounder tensor(0.8820)\n",
      "model: suppressor tensor(0.8272)\n",
      "model: no colour tensor(0.6698)\n",
      "\n",
      "\n",
      "suppressor data:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\clark01\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torchmetrics\\utilities\\prints.py:36: UserWarning: No positive samples in targets, true positive value should be meaningless. Returning zero tensor in true positive score\n",
      "  warnings.warn(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model: confounder tensor(0.7359)\n",
      "model: suppressor tensor(0.8290)\n",
      "model: no colour tensor(0.6780)\n",
      "\n",
      "\n",
      "no colour data:\n",
      "model: confounder tensor(0.8174)\n",
      "model: suppressor tensor(0.8275)\n",
      "model: no colour tensor(0.8754)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "batch_size=64\n",
    "# confounder_test_loader = DataLoader(TensorDataset(torch.tensor(conf_test.transpose(0,3,1,2)), torch.tensor(y_test)),batch_size=batch_size, shuffle=False)\n",
    "# supressor_test_loader = DataLoader(TensorDataset(torch.tensor(sup_test.transpose(0,3,1,2)), torch.tensor(y_test)),batch_size=batch_size, shuffle=False)\n",
    "# no_col_test_loader = DataLoader(TensorDataset(torch.tensor(no_col_test.transpose(0,3,1,2)), torch.tensor(y_test)),batch_size=batch_size, shuffle=False)\n",
    "\n",
    "confounder_data_results = []\n",
    "suppressor_data_results = []\n",
    "no_col_data_results = []\n",
    "\n",
    "for i in range(1):\n",
    "    print(f'MODEL {i}')\n",
    "    print('confounder data:')\n",
    "    confounder_data_results.append(print_AUC(confounder_test_loader, i))\n",
    "    print()\n",
    "    print('suppressor data:')\n",
    "    suppressor_data_results.append(print_AUC(suppressor_test_loader, i))\n",
    "    print()\n",
    "    print('no colour data:')\n",
    "    no_col_data_results.append(print_AUC(no_col_test_loader, i))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a135d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# current problem\n",
    "# scaling channel noise by sum of that channel will colour background in accordance with pattern colour\n",
    "# alternative: scale channel noise according to another factor, i.e. sum(channel_0) = 2*sum(channel_1 or _2)\n",
    "# key aim: keep total mass in red = green + blue\n",
    "# another problem: we have the c6_chunk = (1.0, 1.0, 1.0) where r = g + b; should we just make this 1.0, 0.5, 0.5? it's "
   ]
  }
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