torch.Tensor.requires_grad_
Tensor.requires_grad_(requires_grad=True) -> Tensor
Change if autograd should record operations on this tensor: sets
   this tensor's "requires_grad" attribute in-place. Returns this
   tensor.
"requires_grad_()"'s main use case is to tell autograd to begin
   recording operations on a Tensor "tensor". If "tensor" has
   "requires_grad=False" (because it was obtained through a
   DataLoader, or required preprocessing or initialization),
   "tensor.requires_grad_()" makes it so that autograd will begin to
   record operations on "tensor".
Parameters:
      requires_grad (bool) -- If autograd should record
      operations on this tensor. Default: "True".
Example:
  >>> # Let's say we want to preprocess some saved weights and use
  >>> # the result as new weights.
  >>> saved_weights = [0.1, 0.2, 0.3, 0.25]
  >>> loaded_weights = torch.tensor(saved_weights)
