[Term Entry] PyTorch Tensor Operations: .as_strided()

* [Term Entry] PyTorch Tensor Operations: `.as_strided()`

* Minor changes

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Author: mamtawardhani

content/pytorch/concepts/tensor-operations/terms/as-strided/as-strided.md

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+---
+Title: '.as_strided()'
+Description: 'Creates a view of a tensor with a specified shape and strides.'
+Subjects:
+  - 'AI'
+  - 'Data Science'
+Tags:
+  - 'AI'
+  - 'Data Types'
+  - 'Deep Learning'
+  - 'Functions'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/data-science'
+---
+
+In PyTorch, the **`.as_strided()`** function creates a view of a tensor with a specified shape and strides. Unlike operations that copy data, `.as_strided()` allows modifying how the tensor accesses memory, which enables efficient slicing, reshaping, and advanced indexing without additional memory allocation.
+
+> **Note:** Since `.as_strided()` manipulates tensor memory layout directly, incorrect stride values can lead to unexpected behavior or memory overlap.
+
+## Syntax
+
+```pseudo
+torch.as_strided(input, size, stride, storage_offset=None)
+```
+
+- `input`: The input tensor.
+- `size`: The desired shape of the output tensor.
+- `stride`: A tuple specifying the step size to move across dimensions.
+- `storage_offset` (Optional): Defines the starting point in the underlying storage for the output tensor. If set to `None`, the output tensor retains the same `storage_offset` as the input tensor.
+
+## Example
+
+The following example demonstrates how `.as_strided()` can be used to create a sliding window view of a tensor:
+
+```py
+import torch
+
+# Create a 1D tensor
+tensor = torch.arange(10)
+
+# Create a 2x3 strided view (overlapping windows)
+windowed_tensor = tensor.as_strided((2, 3), (2, 1))
+
+# Print the resultant tensor
+print(windowed_tensor)
+```
+
+This code generates the output as:
+
+```shell
+tensor([[0, 1, 2],
+        [2, 3, 4]])
+```
+
+- The original tensor `tensor` contains values `[0, 1, 2, ..., 9]`.
+- The `.as_strided()` function generates a view where:
+  - The new shape is `(2, 3)`, meaning two rows and three columns.
+  - The first stride is `2`, meaning each row starts 2 elements ahead in the original tensor.
+  - The second stride is `1`, meaning elements within each row are consecutive.