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content/pytorch/concepts/tensor-operations/terms/index-select/index-select.md

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+---
+Title: '.index_select()'
+Description: 'Extracts specific elements from a tensor along a specified dimension based on indices and returns a new tensor.'
+Subjects:
+  - 'AI'
+  - 'Data Science'
+Tags:
+  - 'AI'
+  - 'Arrays'
+  - 'Data Structures'
+  - 'Deep Learning'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/computer-science'
+---
+
+In PyTorch, the **`.index_select()`** function extracts specific elements from a tensor along a specified dimension based on indices and returns a new tensor.
+
+## Syntax
+
+```pseudo
+torch.index_select(input, dim, index, out=None)
+```
+
+- `input`: The input tensor to select elements from.
+- `dim`: The dimension along which to index (e.g., `0` for rows, `1` for columns).
+- `index`: A 1-D tensor containing the indices to select from the specified dimension.
+- `out` (Optional): The output tensor to store the result. If specified, the result will be written to this tensor instead of creating a new one.
+
+> **Note:** If the `out` parameter is provided, the function stores the result in the given tensor. Otherwise, a new tensor is created and returned.
+
+## Example
+
+The following example demonstrates the use of `.index_select()`:
+
+```py
+import torch
+
+# Define a 2D tensor
+ten = torch.tensor([[1, 2, 3, -8],
+                    [4, 3, 8, 0],
+                    [-1, 7, 6, 3],
+                    [5, 6, 9, 0]])
+
+# Define indices to select
+indices = torch.tensor([0, 2])
+
+# Select rows from the tensor (dimension 0)
+r = torch.index_select(ten, 0, indices)
+print("Rows selected:")
+print(r)
+
+# Select columns from the tensor (dimension 1)
+c = torch.index_select(ten, 1, indices)
+print("Columns selected:")
+print(c)
+```
+
+This will generate the output as follows:
+
+```shell
+Rows selected:
+tensor([[ 1,  2,  3, -8],
+        [-1,  7,  6,  3]])
+Columns selected:
+tensor([[ 1,  3],
+        [ 4,  8],
+        [-1,  6],
+        [ 5,  9]])
+```