code: def gail_loss(
      entropy_coefficient: float = 0.0,
      mixup_alpha: Optional[float] = None,
  ) -> Loss:
      """Computes the standard GAIL loss."""
  
      def loss_fn(
          discriminator_fn: DiscriminatorFn,
          discriminator_state: State,
          demo_transitions: types.Transition,
          rb_transitions: types.Transition,
          rng_key: networks_lib.PRNGKey,
      ) -> LossOutput:
          data, labels = _label_data(
              rb_transitions=rb_transitions,
              demonstration_transitions=demo_transitions,
              mixup_alpha=mixup_alpha,
              key=rng_key,
          )
          logits, discriminator_state = discriminator_fn(
              discriminator_state, data
          )
  
          classification_loss = jnp.mean(
              _binary_cross_entropy_loss(logits, labels)
          )
  
          entropy = jnp.mean(_logit_bernoulli_entropy(logits))
          entropy_loss = -entropy_coefficient * entropy
  
          total_loss = classification_loss + entropy_loss
  
          metrics = {
              "total_loss": total_loss,
              "entropy_loss": entropy_loss,
              "classification_loss": classification_loss,
          }
          return total_loss, (
              metrics,
              discriminator_state,
          )  # pytype: disable=bad-return-type  # jnp-type
  
      return loss_fn
  
file_path: acme/acme/agents/jax/ail/losses.py
imports: import functools
  from typing import Callable, Dict, Optional, Tuple
  from acme import types
  from acme.jax import networks
  import jax
  import jax.numpy
  import tensorflow_probability
  import tree
label_desc: Use image node_id 5 for calling a global function with example usage: gail_loss(entropy_coefficient, mixup_alpha) and returns: loss_fn, total_loss,  metrics,  discriminator_state