code: def make_networks(action_spec: specs.BoundedArray):
      """Simple networks for testing.."""
  
      num_dimensions = np.prod(action_spec.shape, dtype=int)
  
      policy_network = snt.Sequential(
          [
              networks.LayerNormMLP([50], activate_final=True),
              networks.NearZeroInitializedLinear(num_dimensions),
              networks.TanhToSpec(action_spec),
          ]
      )
      # The multiplexer concatenates the (maybe transformed) observations/actions.
      critic_network = snt.Sequential(
          [
              networks.CriticMultiplexer(
                  critic_network=networks.LayerNormMLP(
                      [50], activate_final=True
                  )
              ),
              networks.DiscreteValuedHead(-1.0, 1.0, 10),
          ]
      )
  
      return {
          "policy": policy_network,
          "critic": critic_network,
          "observation": tf2_utils.batch_concat,
      }
  
file_path: acme/acme/agents/tf/d4pg/agent_distributed_test.py
imports: import acme
  from acme import specs
  from acme.agents.tf import d4pg
  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