code: def make_networks(action_spec: specs.BoundedArray):
      """Creates simple networks for testing.."""
  
      num_dimensions = np.prod(action_spec.shape, dtype=int)
  
      # Create the observation network shared between the policy and critic.
      observation_network = tf2_utils.batch_concat
  
      # Create the policy network (head) and the evaluation network.
      policy_network = snt.Sequential(
          [
              networks.LayerNormMLP([50], activate_final=True),
              networks.NearZeroInitializedLinear(num_dimensions),
              networks.TanhToSpec(action_spec),
          ]
      )
      evaluator_network = snt.Sequential(
          [observation_network, policy_network]
      )
  
      # Create the critic network.
      critic_network = snt.Sequential(
          [
              # The multiplexer concatenates the observations/actions.
              networks.CriticMultiplexer(),
              networks.LayerNormMLP([50], activate_final=True),
              networks.NearZeroInitializedLinear(1),
          ]
      )
  
      return {
          "policy": policy_network,
          "critic": critic_network,
          "observation": observation_network,
          "evaluator": evaluator_network,
      }
  
file_path: acme/acme/agents/tf/ddpg/agent_distributed_test.py
imports: import acme
  from acme import specs
  from acme.agents.tf import ddpg
  from acme.testing import fakes
  from acme.tf import networks
  from acme.tf import utils
  import launchpad
  import numpy
  import sonnet
  from absl.testing import absltest
label_desc: Use image node_id 1 for calling a global function with example usage: make_networks(action_spec) and returns: dict