pytorch with torch.no_grad() english

In PyTorch, `torch.no_grad()` is a context manager that temporarily disables gradient computation.

In deep learning, gradient computation is used to update the weights of a neural network during training. However, when we evaluate the performance of a model on a test set or use it for prediction, we don't need to compute gradients. Therefore, using `torch.no_grad()` can speed up the computation and reduce memory usage.

When we use `torch.no_grad()`, any computation performed inside the with block will not have gradients computed or stored. This means that we cannot update the weights of the model inside the block, but we can still use the model for evaluation or prediction.

For example, if we have a trained model and a test set, we can use `torch.no_grad()` to evaluate the model on the test set without computing gradients:

with torch.no_grad():
    for input, target in test_set:
        output = model(input)
        loss = loss_fn(output, target)
        total_loss += loss.item()

Note that `torch.no_grad()` is a context manager, so it should be used with the `with` statement. Once we exit the `with` block, gradient computation is enabled again.