How does optimizer work when there are 3 backwards(real, fake, penalty)? about pytorch-wgan HOT 2 CLOSED

                self.D.zero_grad()
                
                z = torch.randn(self.args.batch_size, 1, 96).to(self.args.device)
                tree = [z]
                
                with torch.no_grad():
                    fake_point = self.G(tree)         
                    
                D_real = self.D(point)
                D_realm = D_real.mean()

                D_fake = self.D(fake_point)
                D_fakem = D_fake.mean()

                gp_loss = self.GP(self.D, point.data, fake_point.data)
                
                d_loss = -D_realm + D_fakem
                d_loss_gp = d_loss + gp_loss
                d_loss_gp.backward()
                self.optimizerD.step()