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。

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torch.set_grad_enabled(False)

`torch.set_grad_enabled(False)` 是一个函数，它用于在 PyTorch 中关闭自动求导功能，即关闭梯度计算。当我们不需要计算梯度时，可以使用该函数来提高代码的执行效率，减少内存消耗。例如，当我们只是需要使用一个训练好的模型进行推理时，就可以关闭自动求导功能。

在调用 `torch.set_grad_enabled(False)` 后，所有的计算图都不会计算梯度，即使输入的张量有 `requires_grad=True`。因此，在执行代码时，我们需要确保已经计算完成的张量不需要再进行求导操作。

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.