code: def generator_step(self, batch_size: int, states: elegy.States):
      z = jax.random.normal(states.rng.next(), (batch_size, 128))
  
      def g_loss_fn(g_params, states, z):
          x_fake, g_params, g_states = self.generator.apply(
              g_params, states.g_states
          )(z)
          y_fake_scores = self.discriminator.apply(
              states.d_params, states.d_states
          )(x_fake)[0]
          y_fake_true = jnp.ones(len(z))
          loss = -y_fake_scores.mean()
          return loss, states.update_known(**locals())
  
      (g_loss, states), g_grads = jax.value_and_grad(
          g_loss_fn, has_aux=True
      )(states.g_params, states, z)
      g_grads, g_opt_states = self.g_optimizer.update(
          g_grads, states.g_opt_states, states.g_params
      )
      g_params = optax.apply_updates(states.g_params, g_grads)
  
      return g_loss, states.update_known(**locals())
  
file_path: elegy/examples/need-fixing/WGAN-GP/model.py
imports: import jax
  import jax.numpy
  import optax
  import elegy
label_desc: Use image node_id 6 for calling the WGAN_GP obj's underlying member method code with example usage: obj.generator_step(batch_size, states) and returns: g_loss,  states, loss,  states