The majority of PyTorch operations is differentiable. I’m not sure if there is a list of non-differentiable operations, but in doubt you could check the .grad_fn of the output.
If it points to a valid function, then Autograd will be able to propely backpropagate through it.
Note that recreating new tensors (e.g. via x = torch.tensor(x)) will break the computation graph.