Hi @Shisho_Sama,
For Tensors in most cases, you should go for clone since this is a PyTorch operation that will be recorded by autograd.
>>> t = torch.rand(1, requires_grad=True)
>>> t.clone()
tensor([0.4847], grad_fn=<CloneBackward>) # <=== as you can see here

When it comes to Module, there is no clone method available so you can either use copy.deepcopy or create a new instance of the model and just copy the parameters, as proposed in this post Deep copying PyTorch modules 2.1k.