code: def make_networks(
      spec: specs.EnvironmentSpec,
      policy_layer_sizes: Sequence[int] = (300, 200),
      critic_layer_sizes: Sequence[int] = (400, 300),
      vmin: float = -150.0,
      vmax: float = 150.0,
      num_atoms: int = 51,
  ) -> 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(
                      policy_layer_sizes, activate_final=True
                  ),
                  networks_lib.NearZeroInitializedLinear(
                      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 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/acme/agents/jax/d4pg/networks.py
imports: import dataclasses
  from typing import Sequence
  from acme import specs
  from acme import types
  from acme.agents.jax import actor_core
  from acme.agents.jax.d4pg import config
  from acme.jax import networks
  from acme.jax import utils
  import haiku
  import jax.numpy
  import numpy
  import rlax
label_desc: Use image node_id 3 for calling a global function with example usage: make_networks(spec, policy_layer_sizes, critic_layer_sizes, vmin, vmax, num_atoms) and returns: D4PGNetworks, network, value,  critic_atoms