d_grads = self.calculate_gradients(D)
self.train_d(d_grads)
g_grads = self.calculate_gradients(G)
self.train_g(g_grads)

"trainer": {
  "class": "class:hypergan.trainers.alternating_trainer.AlternatingTrainer",
  "d_optimizer": {
    "class": "class:torch.optim.Adam",
    "lr": 1e-4,
    "betas":[0.0,0.999]
  },
  "g_optimizer": {
    "class": "class:torch.optim.Adam",
    "lr": 1e-4,
    "betas":[0.0,0.999]
  },
  "hooks": [
    {
      "class": "function:hypergan.train_hooks.adversarial_norm_train_hook.AdversarialNormTrainHook",
      "gamma": 1e3,
      "loss": ["d"]
    }
  ]
}