DCGAN understanding generator update step

Here is some DCGAN example in Pytorch: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html#training

I wonder why we don't zero discriminator gradients before we update generator? (I added line in original code # netD.zero_grad() # Q: why we don't do this?) Is it because gradients accumulate in some separate variable and don't affect gradients calculation in generator and only can affect update of weights, but on generator update step we only update generator weights so it's fine?

Here is code section from training loop:

############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
## Train with all-real batch
netD.zero_grad()
# Format batch
real_cpu = data[0].to(device)
b_size = real_cpu.size(0)
label = torch.full((b_size,), real_label, dtype=torch.float, device=device)
# Forward pass real batch through D
output = netD(real_cpu).view(-1)
# Calculate loss on all-real batch
errD_real = criterion(output, label)
# Calculate gradients for D in backward pass
errD_real.backward()
D_x = output.mean().item()

## Train with all-fake batch
# Generate batch of latent vectors
noise = torch.randn(b_size, nz, 1, 1, device=device)
# Generate fake image batch with G
fake = netG(noise)
label.fill_(fake_label)
# Classify all fake batch with D
output = netD(fake.detach()).view(-1)
# Calculate D's loss on the all-fake batch
errD_fake = criterion(output, label)
# Calculate the gradients for this batch, accumulated (summed) with previous gradients
errD_fake.backward()
D_G_z1 = output.mean().item()
# Compute error of D as sum over the fake and the real batches
errD = errD_real + errD_fake
# Update D
optimizerD.step()

############################
# (2) Update G network: maximize log(D(G(z)))
###########################
netG.zero_grad()
# netD.zero_grad() # Q: why we don't do this?
label.fill_(real_label)  # fake labels are real for generator cost
# Since we just updated D, perform another forward pass of all-fake batch through D
output = netD(fake).view(-1)
# Calculate G's loss based on this output
errG = criterion(output, label)
# Calculate gradients for G
errG.backward()
D_G_z2 = output.mean().item()
# Update G
optimizerG.step()

from DCGAN understanding generator update step