Hello
I am very very confused about the gradients when we do back-propagation. According to the Pytorch’s Autograd webpage, it says that if we want a tensor/variable to contain gradient information, we need to set require_grad=True. I have gone through many examples on the web, for instance: reinforce algorithm example  22, or policy gradient example 17, they didn’t set require_grad=True.
I am wondering, in that case, when they do loss.backward(), and they didn’t even specify a single instance that require_grad=True, what exactly is happening? Do they event do back-propagation?