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# 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)
      >>> weights = preprocess(loaded_weights)  # some function
      >>> weights
      tensor([-0.5503,  0.4926, -2.1158, -0.8303])

      >>> # Now, start to record operations done to weights
      >>> weights.requires_grad_()
      >>> out = weights.pow(2).sum()
      >>> out.backward()
      >>> weights.grad
      tensor([-1.1007,  0.9853, -4.2316, -1.6606])

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