train_gen.py

#to train the generator
# Input to generator is a noise of size random_size
z = torch.randn(batch_size, random_size) 
output_image = generator(z)
output_discriminator = discriminator(output_image)

#to train the generator the output of this should be compared with real_labels. 
#so we compare the output by real label. 
#criterion -> BCE Loss
g_loss = criterion(outputs, real_labels)

optimizer_g.zero_grad()
g_loss.backward()
optimizer_g.step()