691e0b50d0b15665df5ec77eeec7c605c4283e1f,hypergan/trainers/alternating_trainer.py,AlternatingTrainer,_step,#AlternatingTrainer#Any#,19

Before Change

        for hook in self.train_hooks:
            loss = hook.forward()
            if loss[0] is not None:
                d_loss += loss[0]


        d_loss.mean().backward()
        self.d_optimizer.step()

        self.g_optimizer.zero_grad()
        d_loss, g_loss = self.gan.forward_loss()

After Change

        d_grads, _ = self.calculate_gradients(["d"])

        for hook in self.train_hooks:
            d_grads, _ = hook.gradients(d_grads, _)
        for p, np in zip(self.gan.d_parameters(), d_grads):
            p.grad = np

Instances