code: def call(self, inputs, step_type=None, network_state=()):
      del step_type
      hidden_state = tf.cast(tf.nest.flatten(inputs), tf.float32)[0]
      for layer in self._dummy_layers:
          hidden_state = layer(hidden_state)
  
      means = tf.reshape(
          hidden_state, [-1] + self._flat_action_spec.shape.as_list()
      )
      spec_means = (
          self._flat_action_spec.maximum
          + self._flat_action_spec.minimum
      ) / 2.0
      spec_ranges = (
          self._flat_action_spec.maximum
          - self._flat_action_spec.minimum
      ) / 2.0
      action_means = spec_means + spec_ranges * means
      if self._emit_distribution:
          action_means = tfd.Deterministic(loc=action_means)
  
      return (
          tf.nest.pack_sequence_as(self._action_spec, [action_means]),
          network_state,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents.ppo import ppo_policy
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import mask_splitter_network
  from tf_agents.networks import network
  from tf_agents.networks import sequential
  from tf_agents.networks import value_network
  from tf_agents.specs import distribution_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
label_desc: Use image node_id 2 for calling the DummyActorNet obj's underlying member method code with example usage: obj.call(inputs, step_type, network_state) and returns: tf,  network_state