code: def _compute_gradient_penalty(
      gradient_penalty_data: types.Transition,
      discriminator_fn: Callable[[types.Transition], float],
      gradient_penalty_target: float,
  ) -> float:
      """Computes a penalty based on the gradient norm on the data."""
      # The input should not be batched.
      assert not gradient_penalty_data.reward.shape
      discriminator_gradient_fn = jax.grad(discriminator_fn)
      gradients = discriminator_gradient_fn(gradient_penalty_data)
      gradients = tree.map_structure(lambda x: x.flatten(), gradients)
      gradients = jnp.concatenate(
          [
              gradients.observation,
              gradients.action,
              gradients.next_observation,
          ]
      )
      gradient_norms = jnp.linalg.norm(gradients + 1e-8)
      k = gradient_penalty_target * jnp.ones_like(gradient_norms)
      return jnp.mean(jnp.square(gradient_norms - k))
  
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 8 for calling a global function with example usage: _compute_gradient_penalty(gradient_penalty_data, discriminator_fn, gradient_penalty_target) and returns: jnp