code: def ail_update_step(
      state: DiscriminatorTrainingState,
      data: Tuple[types.Transition, types.Transition],
      optimizer: optax.GradientTransformation,
      ail_network: ail_networks.AILNetworks,
      loss_fn: losses.Loss,
  ) -> Tuple[DiscriminatorTrainingState, losses.Metrics]:
      """Run an update steps on the given transitions.
  
      Args:
        state: The learner state.
        data: Demo and rb transitions.
        optimizer: Discriminator optimizer.
        ail_network: AIL networks.
        loss_fn: Discriminator loss to minimize.
  
      Returns:
        A new state and metrics.
      """
      demo_transitions, rb_transitions = data
      key, discriminator_key, loss_key = jax.random.split(state.key, 3)
  
      def compute_loss(
          discriminator_params: networks_lib.Params,
      ) -> losses.LossOutput:
          discriminator_fn = functools.partial(
              ail_network.discriminator_network.apply,
              discriminator_params,
              state.policy_params,
              is_training=True,
              rng=discriminator_key,
          )
          return loss_fn(
              discriminator_fn,
              state.discriminator_state,
              demo_transitions,
              rb_transitions,
              loss_key,
          )
  
      loss_grad = jax.grad(compute_loss, has_aux=True)
  
      grads, (loss, new_discriminator_state) = loss_grad(
          state.discriminator_params
      )
  
      update, optimizer_state = optimizer.update(
          grads,
          state.optimizer_state,
          params=state.discriminator_params,
      )
      discriminator_params = optax.apply_updates(
          state.discriminator_params, update
      )
  
      new_state = DiscriminatorTrainingState(
          optimizer_state=optimizer_state,
          discriminator_params=discriminator_params,
          discriminator_state=new_discriminator_state,
          policy_params=state.policy_params,  # Not modified.
          key=key,
          steps=state.steps + 1,
      )
      return new_state, loss
  
imports: import functools
  import itertools
  import time
  from typing import Any, Callable, Iterator, List, NamedTuple, Optional, Tuple
  import acme
  from acme import types
  from acme.agents.jax.ail import losses
  from acme.agents.jax.ail import networks
  from acme.jax import networks
  from acme.jax import utils
  from acme.utils import counting
  from acme.utils import loggers
  from acme.utils import reverb_utils
  import jax
  import optax
  import reverb
label_desc: Use image node_id 1 for calling a global function with example usage: ail_update_step(state, data, optimizer, ail_network, loss_fn) and returns: new_state,  loss, loss_fn