code: def make_networks(
      spec: specs.EnvironmentSpec,
      policy_layer_sizes: Sequence[int] = (256, 256, 256),
      critic_layer_sizes: Sequence[int] = (512, 512, 256),
      vmin: float = -150.0,
      vmax: float = 150.0,
      num_atoms: int = 201,
  ) -> d4pg.D4PGNetworks:
      """Creates networks used by the agent."""
  
      action_spec = spec.actions
  
      num_dimensions = np.prod(action_spec.shape, dtype=int)
      critic_atoms = jnp.linspace(vmin, vmax, num_atoms)
  
      def _actor_fn(obs):
          network = hk.Sequential(
              [
                  utils.batch_concat,
                  networks_lib.LayerNormMLP(
                      list(policy_layer_sizes) + [num_dimensions]
                  ),
                  networks_lib.TanhToSpec(action_spec),
              ]
          )
          return network(obs)
  
      def _critic_fn(obs, action):
          network = hk.Sequential(
              [
                  utils.batch_concat,
                  networks_lib.LayerNormMLP(
                      layer_sizes=[*critic_layer_sizes, num_atoms]
                  ),
              ]
          )
          value = network([obs, action])
          return value, critic_atoms
  
      policy = hk.without_apply_rng(hk.transform(_actor_fn))
      critic = hk.without_apply_rng(hk.transform(_critic_fn))
  
      # Create dummy observations and actions to create network parameters.
      dummy_action = utils.zeros_like(spec.actions)
      dummy_obs = utils.zeros_like(spec.observations)
      dummy_action = utils.add_batch_dim(dummy_action)
      dummy_obs = utils.add_batch_dim(dummy_obs)
  
      return d4pg.D4PGNetworks(
          policy_network=networks_lib.FeedForwardNetwork(
              lambda rng: policy.init(rng, dummy_obs), policy.apply
          ),
          critic_network=networks_lib.FeedForwardNetwork(
              lambda rng: critic.init(rng, dummy_obs, dummy_action),
              critic.apply,
          ),
      )
  
file_path: acme/examples/baselines/imitation/run_pwil.py
imports: from typing import Sequence
  from absl import flags
  from acme import specs
  from acme.agents.jax import d4pg
  from acme.agents.jax import pwil
  from acme.datasets import tfds
  import helpers
  from absl import app
  from acme.jax import experiments
  from acme.jax import networks
  from acme.jax import utils
  from acme.utils import lp_utils
  import dm_env
  import haiku
  import jax.numpy
  import launchpad
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: make_networks(spec, policy_layer_sizes, critic_layer_sizes, vmin, vmax, num_atoms) and returns: d4pg, network, value,  critic_atoms