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+---
+Title: '.narrow()'
+Description: 'Selects a narrow subsection of a tensor along the specified dimension.'
+Subjects:
+  - 'Computer Science'
+  - 'Machine Learning'
+  - 'Data Science'
+Tags:
+  - 'Python'
+  - 'Machine Learning'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/computer-science'
+---
+
+In PyTorch, the **`.narrow()`** function is used to select a subsection of a tensor along a specified dimension. It returns a narrowed view without copying data. It is useful for extracting specific sections of large tensors without creating new memory allocations.
+
+## Syntax
+
+```pseudo
+torch.narrow(tensor, dim, start, length)
+```
+
+- `tensor`: The tensor to be narrowed.
+- `dim`: The dimension along which the input tensor is narrowed.
+- `start`: The starting index where the narrowing begins. This can be a positive integer, a negative integer (to index from the end of `dim`), or a 0-dimensional integer tensor.
+- `length`: The number of elements to include from the starting position.
+
+## Example
+
+The following example illustrates the usage of `.narrow()` function in various scenarios:
+
+```py
+import torch
+
+# Create a 2D tensor
+tensor_2d = torch.arange(1, 13).reshape(3, 4)
+print(f"Original 2D Tensor:\n{tensor_2d}")
+
+# Case 1: Narrowing along rows (dim=0)
+row_narrow = torch.narrow(tensor_2d, 0, 1, 2)
+print("\nCase 1: Narrow Along Rows (dim=0, start=1, length=2)")
+print(row_narrow)
+
+# Case 2: Narrowing along columns (dim=1)
+col_narrow = torch.narrow(tensor_2d, 1, 1, 2)
+print("\nCase 2: Narrow Along Columns (dim=1, start=1, length=2)")
+print(col_narrow)
+
+# Case 3: Extracting a single column (dim=1, length=1)
+single_col = torch.narrow(tensor_2d, 1, 2, 1)
+print("\nCase 3: Extract Single Column (dim=1, start=2, length=1)")
+print(single_col)
+
+# Case 4: Narrow with length extending beyond tensor's dimension
+# In this case, .narrow() raises an error because the sub-tensor's length exceeds the tensor's dimension
+try:
+    error_narrow = torch.narrow(tensor_2d, 0, 1, 5)
+    print("\nCase 4: Narrow With Length Exceeding Dimension Size")
+    print(error_narrow)
+except RuntimeError as e:
+    print("\nCase 4: RuntimeError -", e)
+
+# Case 5: Using a negative start index (dim=1, start=-3, length=2)
+negative_start_narrow = torch.narrow(tensor_2d, 1, -3, 2)
+print("\nCase 5: Negative Start Index (dim=1, start=-3, length=2)")
+print(negative_start_narrow)
+
+# Case 6: Using a 0-dimensional start index
+tensor_0_dim = torch.tensor(1)  # 0-dimensional integer tensor
+tensor_start_narrow = torch.narrow(tensor_2d, 0, tensor_0_dim, 2)
+print("\nCase 6: Start Index as a 0-Dim Tensor (dim=0, start=tensor(1), length=2)")
+print(tensor_start_narrow)
+```
+
+The above program gives the following output:
+
+```shell
+Original 2D Tensor:
+tensor([[ 1,  2,  3,  4],
+        [ 5,  6,  7,  8],
+        [ 9, 10, 11, 12]])
+
+Case 1: Narrow Along Rows (dim=0, start=1, length=2)
+tensor([[ 5,  6,  7,  8],
+        [ 9, 10, 11, 12]])
+
+Case 2: Narrow Along Columns (dim=1, start=1, length=2)
+tensor([[ 2,  3],
+        [ 6,  7],
+        [10, 11]])
+
+Case 3: Extract Single Column (dim=1, start=2, length=1)
+tensor([[ 3],
+        [ 7],
+        [11]])
+
+Case 4: RuntimeError - start (1) + length (5) exceeds dimension size (3).
+
+Case 5: Negative Start Index (dim=1, start=-3, length=2)
+tensor([[ 2,  3],
+        [ 6,  7],
+        [10, 11]])
+
+Case 6: Start Index as a 0-Dim Tensor (dim=0, start=tensor(1), length=2)
+tensor([[ 5,  6,  7,  8],
+        [ 9, 10, 11, 12]])
+```