In the DCGAN example that can be found here 282, while training the generator network after training the discriminator network, we do not perform netG.zero_grad() again.  However, doesn’t this accumulate the gradients with respect to real data in the netD (line 208), or the gradients with respect to the previous feeding of fake data (line 217)?  Does the former not happen because the input tensor is different (real/fake), and does the latter not happen because we had performed a detach (line 215)?