[Term Entry] PyTorch Tensor Operations: .log1p()

* Create log1p entry for pytorch

* Update log1p.md

* minor content fixes

* Minor changes

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Author: arisdelacruz

content/pytorch/concepts/tensor-operations/terms/log1p/log1p.md

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+---
+Title: '.log1p()'
+Description: 'Computes the natural logarithm of one plus the input tensor element-wise.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Data Structures'
+  - 'Functions'
+  - 'Index'
+  - 'Values'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/computer-science'
+---
+
+In PyTorch, **`.log1p()`** computes the natural logarithm of one plus the input [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors) element-wise.
+
+This function improves numerical stability for small input values, avoiding issues that can occur when computing the logarithm directly.
+
+## Syntax
+
+```pseudo
+torch.log1p(input, *, out=None)
+```
+
+**Parameters:**
+
+- `input`: The input tensor for which the natural logarithm of one plus the elements will be computed.
+- `out` (Optional): A tensor to store the result. If provided, the computed values will be written to this tensor instead of creating a new one.
+
+**Return value:**
+
+Returns a new tensor where each element is the natural logarithm of one plus the corresponding element in the input tensor. The original tensor remains unchanged unless an `out` tensor is provided.
+
+## Example
+
+In this example, `.log1p()` computes the natural logarithm of one plus each element in a tensor:
+
+```py
+import torch
+
+# Create a tensor
+x = torch.tensor([0.0, 1.0, 2.0])
+
+# Compute log1p
+y = torch.log1p(x)
+
+print(y)
+```
+
+The code above generates the output as follows:
+
+```shell
+tensor([0.0000, 0.6931, 1.0986])
+```
+
+In this example, the input tensor `x` contains the values `[0.0, 1.0, 2.0]`. The `log1p()` function computes the natural logarithm of one plus each element in the tensor, resulting in the output tensor `[0.0000, 0.6931, 1.0986]`.