code: def discriminator_step(
      self, x_real: jnp.ndarray, states: elegy.States
  ):
      z = jax.random.normal(states.rng.next(), (len(x_real), 128))
      x_fake = self.generator.apply(states.g_params, states.g_states)(
          z
      )[0]
  
      def d_loss_fn(d_params, states, x_real, x_fake):
          y_real, d_params, d_states = self.discriminator.apply(
              d_params, states.d_states
          )(x_real)
          y_fake, d_params, d_states = self.discriminator.apply(
              d_params, d_states
          )(x_fake)
          loss = -y_real.mean() + y_fake.mean()
          gp = gradient_penalty(
              x_real,
              x_fake,
              self.discriminator.apply(d_params, d_states),
              states.rng.next(),
          )
          loss = loss + gp
          return loss, (gp, states.update_known(**locals()))
  
      (d_loss, (gp, states)), d_grads = jax.value_and_grad(
          d_loss_fn, has_aux=True
      )(states.d_params, states, x_real, x_fake)
      d_grads, d_opt_states = self.d_optimizer.update(
          d_grads, states.d_opt_states, states.d_params
      )
      d_params = optax.apply_updates(states.d_params, d_grads)
  
      return d_loss, gp, 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 5 for calling the WGAN_GP obj's underlying member method code with example usage: obj.discriminator_step(x_real, states) and returns: d_loss,  gp,  states, loss,  gp,  states