with torch.no_grad():

`with torch.no_grad()` is a context manager in PyTorch that disables gradient computation. This means that any operations that are performed within this context will not have their gradients computed or stored for backpropagation. This is useful in situations where you only want to perform forward pass computations and don't want to waste memory storing gradients that you won't use.

For example, if you're only interested in using a pre-trained model for inference, you can wrap your forward pass code with `with torch.no_grad():` to save memory and speed up inference, since you don't need to compute gradients during inference.

Here's an example of how to use `with torch.no_grad():`:

import torch

# Define a tensor with requires_grad=True
x = torch.randn(1, 10, requires_grad=True)

# Perform some operations within a no_grad context
with torch.no_grad():
    y = x * 2
    z = y.mean()

# Since we're outside of the no_grad context, gradients will be computed
z.backward()