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

content/pytorch/concepts/tensor-operations/terms/nonzero/nonzero.md

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+---
+Title: '.nonzero()'
+Description: 'Returns a tensor containing the indices of non-zero elements in the input tensor.'
+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 **`.nonzero()`** function returns a tensor of indices of all non-zero elements in the input [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors). This function is useful for identifying the locations of non-zero values in sparse tensors or when performing operations based on the positions of non-zero elements.
+
+## Syntax
+
+```pseudo
+torch.nonzero(input, *, out=None, as_tuple=False)
+```
+
+- `input`: The input tensor for which the indices of non-zero elements are to be found.
+- `out` (Optional): A tensor to store the result. It must have the appropriate shape to accommodate the output. If not provided, a new tensor will be allocated to store the result.
+- `as_tuple` (Optional): If set to `True`, the result will be a tuple of 1-D tensors, each containing the indices for a specific dimension of the input tensor. If set to `False` (default), the result will be a 2-D tensor where each row represents the index of a non-zero element in the input tensor.
+
+## Example
+
+The following example demonstrates the usage of the `.nonzero()` function:
+
+```py
+import torch
+
+# Create a tensor
+ten = torch.tensor([[1, 0, 3], [0, 5, 6]])
+
+# Get the indices of non-zero elements
+indices = torch.nonzero(ten)
+
+# Print the result
+print(indices)
+```
+
+The above code produces the following output:
+
+```shell
+tensor([[0, 0],
+        [0, 2],
+        [1, 1],
+        [1, 2]])
+```
+
+In this example, the tensor's non-zero elements are located at indices _(0,0)_, _(0,2)_, _(1,1)_, and _(1,2)_.