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+---
+Title: '.exp2()'
+Description: 'Returns a new tensor with the base-2 exponential of each element in the input tensor.'
+Subjects:
+  - 'Computer Science'
+  - 'Machine Learning'
+Tags:
+  - 'Deep Learning'
+  - 'Functions'
+  - 'Tensor'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/computer-science'
+---
+
+The **`.exp2()`** function in PyTorch computes the base-2 exponential of each element in the input [tensor](https://www.codecademy.com/resources/docs/pytorch/tensors). This function is useful for various mathematical operations and can be applied to tensors of any shape. The result is a new tensor with the same shape as the input, containing the base-2 exponentials of the input values.
+
+## Syntax
+
+```pseudo
+# Function syntax:
+result = torch.exp2(input, *, out=None)
+
+# Tensor method syntax:
+result = tensor.exp2()
+```
+
+This function is an alias for:
+
+```pseudo
+torch.special.exp2(input, *, out=None)
+```
+
+**Parameters:**
+
+- input (Tensor): The input tensor
+- out (Tensor, optional): The output tensor to store the result
+
+**Return value:**
+
+The `.exp2()` function returns a tensor with the base-2 exponentials of the input tensor, same shape as the input.
+
+## Example
+
+Here's an example of how to use the `.exp2()` function in PyTorch:
+
+```py
+import torch
+
+# Create a tensor
+x = torch.tensor([1.0, 2.0, 3.0])
+
+# Compute the base-2 exponential
+y = x.exp2()
+
+print(y)
+```
+
+The output will be:
+
+```shell
+tensor([2., 4., 8.])
+```
+
+In this example, the `.exp2()` function is applied to the tensor `x`, and the result is stored in the tensor `y`. The output will be a tensor containing the base-2 exponentials of the elements in `x`.