Hello all! I have a question about the autograd, please look at the following the example.

out = net(input)
out = out.detach().numpy()
new_output = function_1(out)  # function_1 contains some numpy operations
new_output = torch.from_numpy(new_output ).float()
new_output  = Variable(new_output , requires_grad=True).to(device)
loss = loss_function(new_output, label)
loss.backward

When I call ‘loss.backward’, will the parameters in net also be updated?
Thanks