Merge branch 'main' into feat/add-numpy-byteswap-term

Author: prakhar14-op

content/pytorch/concepts/tensor-operations/terms/logaddexp2/logaddexp2.md

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+---
+Title: '.logaddexp2()'
+Description: 'Computes the logarithm of the sum of exponentials of two tensors in base 2.'
+Subjects:
+  - 'Code Foundations'
+  - 'Computer Science'
+Tags:
+  - 'PyTorch'
+  - 'Tensor'
+CatalogContent:
+  - 'learn-pytorch'
+  - 'intro-to-py-torch-and-neural-networks'
+---
+
+In PyTorch, the **`.logaddexp2()`** function computes the element-wise $$log_2(2^x + 2^y)$$ of the given [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors) inputs.
+
+## Syntax
+
+```pseudo
+torch.logaddexp2(input,other,*,out=None)
+```
+
+**Parameters:**
+
+- `input`: The first input tensor
+- `other`: The second input tensor
+- `out`: The output tensor to store the result. Default is `None`.
+
+**Return value:**
+
+The `torch.logaddexp2()` method returns a tensor containing the element-wise logarithm (base 2) of the sum of exponentials of the corresponding elements in the input tensors.
+
+## Example
+
+In this example, `torch.logaddexp2()` is used to combine two tensors into a single tensor by computing their log-sum in base 2:
+
+```py
+import torch
+
+# Example tensors
+x = torch.tensor([1.0, 2.0, 3.0])
+y = torch.tensor([0.5, 2.5, 1.5])
+
+# Compute logaddexp2
+result = torch.logaddexp2(x, y)
+print(result)
+```
+
+This code will produce the following output:
+
+```shell
+tensor([1.7716, 3.2716, 3.4368])
+```