Let’s take a closer look at the detach() function in PyTorch, which plays a helpful role when working with Tensors. The detach() function creates a new Tensor that shares the same data as the original one but without the attached computation history. This essentially separates the new Tensor from the computation graph, making it independent of the original Tensor’s gradient calculation.

The detach() function is particularly handy when you need a Tensor for purposes that don’t require gradient computation. For instance, when using the output of a neural network for evaluation, visualization, or any other purpose that doesn’t involve backpropagation. In these scenarios, the detach() function allows you to obtain a Tensor that’s free from unnecessary dependencies and ready for your desired task.

```
import torch
# Creating a Tensor with requires_grad=True (default is False)
x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
# Performing an operation on the Tensor
y = x * 2
# Detaching the Tensor
y_detached = y.detach()
print("y:", y)
print("y_detached:", y_detached)
```

In this example, `y`

is a Tensor with gradient computation enabled, and `y_detached`

is a new Tensor that shares the same data as `y`

but without gradient computation. You can use `y_detached`

for tasks that don’t need gradients, which can save memory and computation resources.