torch.set_printoptions(linewidth=120) torch.set_grad_enabled(True)

`torch.set_printoptions(linewidth=120)` 是一个 PyTorch 中的函数，它用于设置在控制台输出张量时的行宽。默认情况下，PyTorch 在控制台输出张量时的行宽为 80，如果张量的维度很高，可能会导致输出格式混乱。通过调用 `torch.set_printoptions(linewidth=120)`，可以将控制台输出张量时的行宽设置为 120，从而使输出更加清晰。

`torch.set_grad_enabled(True)` 是一个 PyTorch 中的函数，它用于开启或关闭自动求导功能。当调用 `torch.set_grad_enabled(True)` 时，PyTorch 将启用自动求导功能，即计算张量的梯度。反之，当调用 `torch.set_grad_enabled(False)` 时，PyTorch 将禁用自动求导功能，即不计算张量的梯度。这个函数默认是开启的，但有时可能需要手动关闭自动求导功能，以提高代码的效率。

相关问题

torch.set_grad_enabled(true)

torch.set_grad_enabled(True) 是一个PyTorch中的函数，它用于启用或禁用梯度计算。如果设置为True，梯度计算将被启用；如果设置为False，则梯度计算将被禁用。该函数常常用在训练模型时，在前向传递和反向传递之间进行切换，以节省计算资源。

with torch.set_grad_enabled(enable_grad):

This is a context manager in PyTorch that enables or disables gradient computation.

When `enable_grad` is set to `True`, any computation performed within the context of this manager that involves tensors will have their gradients computed and stored for backpropagation during training. This is the default behavior of PyTorch.

When `enable_grad` is set to `False`, any computation performed within the context of this manager will not have their gradients computed and stored. This is useful when you want to perform inference on a trained model without updating its weights.

For example, consider the following code snippet:

with torch.set_grad_enabled(enable_grad=True):
    # computation involving tensors with gradients enabled

with torch.set_grad_enabled(enable_grad=False):
    # computation involving tensors with gradients disabled

In the first block, gradient computation is enabled for all tensors involved in the computation. In the second block, gradient computation is disabled for all tensors involved in the computation.