code: def _make_gradient_penalty_data(
      rb_transitions: types.Transition,
      demonstration_transitions: types.Transition,
      key: networks_lib.PRNGKey,
  ) -> types.Transition:
      lambdas = tfd.Uniform().sample(
          len(rb_transitions.reward), seed=key
      )
      return tree.map_structure(
          lambda x, y: _weighted_average(x, y, lambdas),
          rb_transitions,
          demonstration_transitions,
      )
  
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 7 for calling a global function with example usage: _make_gradient_penalty_data(rb_transitions, demonstration_transitions, key) and returns: tree