feat(docs): Add PyTorch logical_or term entry (closes #7786)

content/pytorch/concepts/tensor-operations/terms/logical-or/logical-or.md

@@ -0,0 +1,70 @@
+---
+title: logical_or()
+description: Performs an element-wise logical OR operation between two tensors.
+subjects: PyTorch, Tensor Operations, Logical Operations
+tags: PyTorch, Tensor
+---
+
+The `logical_or()` operation performs an element-wise logical OR between two tensors. For each corresponding element pair, it returns _True_ if _either_ element is _True_, and _False_ only if _both_ elements are _False_.
+
+This operation can be used either as a function in the torch module or as a tensor method.
+
+## Syntax
+
+1. **Function form:**
+
+   ```python
+   torch.logical_or(input, other, out=None)
+   ```
+
+2. **Tensor method form:**
+
+   ```python
+   input.logical_or(other, out=None)
+   ```
+
+**Parameters:**
+
+- `input` (Tensor): The first tensor to compare.
+- `other` (Tensor): The second tensor to compare.
+- `out` (Tensor, optional): Tensor to store the output.
+
+## Codebyte Example
+
+```codebyte/python
+import torch
+
+# Example 1: Using boolean tensors
+tensor1 = torch.tensor([True, False, True, False])
+tensor2 = torch.tensor([True, True, False, False])
+
+result = torch.logical_or(tensor1, tensor2)
+print(f"Example 1 Result:\n{result}\n")
+# Output:
+# Example 1 Result:
+# tensor([True, True, True, False])
+
+# Example 2: Using integer tensors
+# Non-zero values are treated as True, and 0 as False.
+tensor_a = torch.tensor([1, 0, 0, 7])
+tensor_b = torch.tensor([0, 0, 3, 0])
+
+result_b = tensor_a.logical_or(tensor_b)
+print(f"Example 2 Result:\n{result_b}\n")
+# Output:
+# Example 2 Result:
+# tensor([True, False, True, True])
+
+# Example 3: Broadcasting
+# A 1D tensor is broadcast to match the 2D tensor's shape.
+tensor_2d = torch.tensor([[True, False, True],
+                        [False, False, False]])
+tensor_1d = torch.tensor([True, False, False])
+
+result_c = torch.logical_or(tensor_2d, tensor_1d)
+print(f"Example 3 (Broadcasting) Result:\n{result_c}")
+# Output:
+# Example 3 (Broadcasting) Result:
+# tensor([[ True, False,  True],
+#         [ True, False, False]])
+```