From b87d7338aa377266554bfc112b4e7a0407fb4826 Mon Sep 17 00:00:00 2001
From: Yuval Levental <yhl3051@rit.edu>
Date: Mon, 15 Dec 2025 12:50:54 +0000
Subject: [PATCH 1/2] (Type what you did)

---
 .../tensor-operations/terms/sgn/sgn.md        | 101 ++++++++++++++++++
 1 file changed, 101 insertions(+)
 create mode 100644 content/pytorch/concepts/tensor-operations/terms/sgn/sgn.md

diff --git a/content/pytorch/concepts/tensor-operations/terms/sgn/sgn.md b/content/pytorch/concepts/tensor-operations/terms/sgn/sgn.md
new file mode 100644
index 00000000000..6533b667a8d
--- /dev/null
+++ b/content/pytorch/concepts/tensor-operations/terms/sgn/sgn.md
@@ -0,0 +1,101 @@
+---
+Title: '.sgn()'
+Description: 'Computes the sign of each element in the input tensor, returning a tensor with the same shape.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Deep Learning'
+  - 'Methods'
+  - 'PyTorch'
+  - 'Tensor'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/data-science'
+---
+
+The **`.sgn()`** function computes the sign of each element in the input [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors), applied element-wise. For real-valued tensors, it returns -1 for negative values, 0 for zero, and 1 for positive values. For complex-valued tensors, it returns the complex sign (the tensor divided by its absolute value), which gives the unit vector in the direction of each complex number.
+
+## Syntax
+
+```pseudo
+torch.sgn(input, *, out=None) → Tensor
+```
+
+**Parameters:**
+
+- `input` (Tensor): The input tensor (can be real or complex).
+- `out` (Tensor, optional): Optional output tensor to store the result.
+
+**Return value:**
+
+A tensor with the same shape as `input`, containing the sign of each element.
+
+## Example 1: Using `.sgn()` with a Real-Valued Tensor
+
+In this example, `.sgn()` computes the sign of each element in a real-valued tensor:
+
+```py
+import torch
+
+# Create a tensor with positive, negative, and zero values
+x = torch.tensor([-5.0, -2.5, 0.0, 2.5, 5.0])
+
+# Compute the sign
+result = torch.sgn(x)
+
+print(result)
+```
+
+The output of this code is:
+
+```shell
+tensor([-1., -1.,  0.,  1.,  1.])
+```
+
+## Example 2: Using `.sgn()` with a 2D Tensor
+
+In this example, `.sgn()` is applied to a 2D tensor:
+
+```py
+import torch
+
+# Create a 2x3 tensor
+matrix = torch.tensor([[-3.0, -1.0, 0.0], [1.0, 2.0, 3.0]])
+
+# Compute the sign
+result = torch.sgn(matrix)
+
+print(result)
+```
+
+The output of this code is:
+
+```shell
+tensor([[-1., -1.,  0.],
+        [ 1.,  1.,  1.]])
+```
+
+## Example 3: Using `.sgn()` with Complex Numbers
+
+For complex-valued tensors, `.sgn()` returns the complex sign, which is the unit vector in the direction of each complex number (computed as `x / |x|`):
+
+```py
+import torch
+
+# Create a tensor with complex numbers
+z = torch.tensor([1+2j, -1+2j, 3-4j])
+
+# Compute the complex sign
+result = torch.sgn(z)
+
+print(result)
+```
+
+The output of this code is:
+
+```shell
+tensor([0.4472+0.8944j, -0.4472+0.8944j, 0.6000-0.8000j])
+```
+
+In this example, each result has a magnitude of 1 (a unit vector), pointing in the direction of the original complex number.

From d2d0979a917b6948155f65ced3c0f4f25be53b38 Mon Sep 17 00:00:00 2001
From: Mamta Wardhani <mamta.wardhani@gmail.com>
Date: Wed, 17 Dec 2025 16:11:21 +0530
Subject: [PATCH 2/2] Update example header for .sgn() in 2D tensor

Clarify the description of the second example for .sgn() usage.
---
 content/pytorch/concepts/tensor-operations/terms/sgn/sgn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/pytorch/concepts/tensor-operations/terms/sgn/sgn.md b/content/pytorch/concepts/tensor-operations/terms/sgn/sgn.md
index 6533b667a8d..aace812a033 100644
--- a/content/pytorch/concepts/tensor-operations/terms/sgn/sgn.md
+++ b/content/pytorch/concepts/tensor-operations/terms/sgn/sgn.md
@@ -53,7 +53,7 @@ The output of this code is:
 tensor([-1., -1.,  0.,  1.,  1.])
 ```
 
-## Example 2: Using `.sgn()` with a 2D Tensor
+## Example 2: Applying `.sgn()` element-wise to a 2D tensor
 
 In this example, `.sgn()` is applied to a 2D tensor: