code: def make_networks(
      action_spec: specs.BoundedArray,
      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 = 51,
  ) -> Dict[str, types.TensorTransformation]:
      """Creates networks used by the agent."""
  
      num_dimensions = np.prod(action_spec.shape, dtype=int)
  
      policy_network = snt.Sequential(
          [
              networks.LayerNormMLP(
                  policy_layer_sizes, activate_final=True
              ),
              networks.MultivariateNormalDiagHead(
                  num_dimensions,
                  tanh_mean=False,
                  init_scale=1.0,
                  fixed_scale=False,
                  use_tfd_independent=True,
              ),
          ]
      )
  
      # The multiplexer concatenates the (maybe transformed) observations/actions.
      critic_network = networks.CriticMultiplexer(
          critic_network=networks.LayerNormMLP(
              critic_layer_sizes, activate_final=True
          ),
          action_network=networks.ClipToSpec(action_spec),
      )
      critic_network = snt.Sequential(
          [
              critic_network,
              networks.DiscreteValuedHead(vmin, vmax, num_atoms),
          ]
      )
      observation_network = networks.ResNetTorso()
  
      return {
          "policy": policy_network,
          "critic": critic_network,
          "observation": observation_network,
      }
  
file_path: acme/examples/tf/control_suite/lp_dmpo_pixels.py
imports: import functools
  from typing import Dict, Sequence
  from absl import app
  from absl import flags
  from acme import specs
  from acme import types
  from acme.agents.tf import dmpo
  import helpers
  from acme.tf import networks
  import launchpad
  import numpy
  import sonnet
label_desc: Use image node_id 1 for calling a global function with example usage: make_networks(action_spec, policy_layer_sizes, critic_layer_sizes, vmin, vmax, num_atoms) and returns: dict