


    return grad_output
  >>>
  >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone()
  >>> b = a * a
  >>> Inplace.apply(a)  # This would lead to wrong gradients!
  >>>                   # but the engine would not know unless we mark_dirty
  >>> b.backward() # RuntimeError: one of the variables needed for gradient
  >>>              # computation has been modified by an inplace operation



