code: def compute_ail_reward(
      discriminator_params: networks_lib.Params,
      discriminator_state: State,
      policy_params: Optional[networks_lib.Params],
      transitions: types.Transition,
      networks: AILNetworks,
  ) -> jnp.ndarray:
      """Computes the AIL reward for a given transition.
  
      Args:
        discriminator_params: Parameters of the discriminator network.
        discriminator_state: State of the discriminator network.
        policy_params: Parameters of the direct RL policy.
        transitions: Transitions to compute the reward for.
        networks: AIL networks.
  
      Returns:
        The rewards as an ndarray.
      """
      logits, _ = networks.discriminator_network.apply(
          discriminator_params,
          policy_params,
          discriminator_state,
          transitions,
          is_training=False,
          rng=None,
      )
      return networks.imitation_reward_fn(logits)
  
imports: import dataclasses
  import functools
  from typing import Any, Callable, Generic, Iterable, Optional
  from acme import specs
  from acme import types
  from acme.jax import networks
  from acme.jax import utils
  from acme.jax.imitation_learning_types import DirectRLNetworks
  import haiku
  import jax
  from jax import numpy
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: compute_ail_reward(discriminator_params, discriminator_state, policy_params, transitions, networks) and returns: networks