code: def add_gradient_penalty(
      base_loss: Loss,
      gradient_penalty_coefficient: float,
      gradient_penalty_target: float,
  ) -> Loss:
      """Adds a gradient penalty to the base_loss."""
  
      if not gradient_penalty_coefficient:
          return base_loss
  
      def loss_fn(
          discriminator_fn: DiscriminatorFn,
          discriminator_state: State,
          demo_transitions: types.Transition,
          rb_transitions: types.Transition,
          rng_key: networks_lib.PRNGKey,
      ) -> LossOutput:
          super_key, gradient_penalty_key = jax.random.split(rng_key)
  
          partial_loss, (losses, discriminator_state) = base_loss(
              discriminator_fn,
              discriminator_state,
              demo_transitions,
              rb_transitions,
              super_key,
          )
  
          gradient_penalty_data = _make_gradient_penalty_data(
              rb_transitions=rb_transitions,
              demonstration_transitions=demo_transitions,
              key=gradient_penalty_key,
          )
  
          def apply_discriminator_fn(
              transitions: types.Transition,
          ) -> float:
              logits, _ = discriminator_fn(
                  discriminator_state, transitions
              )
              return logits  # pytype: disable=bad-return-type  # jax-ndarray
  
          gradient_penalty = gradient_penalty_coefficient * jnp.mean(
              _compute_gradient_penalty(
                  gradient_penalty_data,
                  apply_discriminator_fn,
                  gradient_penalty_target,
              )
          )
  
          losses["gradient_penalty"] = gradient_penalty
          total_loss = partial_loss + gradient_penalty
          losses["total_loss"] = total_loss
  
          return total_loss, (
              losses,
              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 9 for calling a global function with example usage: add_gradient_penalty(base_loss, gradient_penalty_coefficient, gradient_penalty_target) and returns: loss_fn, base_loss, total_loss,  losses,  discriminator_state, logits