code: def __init__(
      self,
      input_spec,
      action_spec,
      preprocessing_layers=None,
      name=None,
  ):
      output_spec = self._get_normal_distribution_spec(action_spec)
      super(DummyActorNet, self).__init__(
          input_spec, (), output_spec=output_spec, name="DummyActorNet"
      )
      self._action_spec = action_spec
      self._flat_action_spec = tf.nest.flatten(self._action_spec)[0]
  
      self._dummy_layers = (preprocessing_layers or []) + [
          tf.keras.layers.Dense(
              self._flat_action_spec.shape.num_elements() * 2,
              kernel_initializer=tf.constant_initializer(
                  [[2.0, 1.0], [1.0, 1.0]]
              ),
              bias_initializer=tf.constant_initializer([5.0, 5.0]),
              activation=None,
          )
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents.cql import cql_sac_agent
  from tf_agents.agents.ddpg import critic_network
  from tf_agents.agents.ddpg import critic_rnn_network
  from tf_agents.agents.sac import tanh_normal_projection_network
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import actor_distribution_rnn_network
  from tf_agents.networks import network
  from tf_agents.networks import utils
  from tf_agents.specs import distribution_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
  from tf_agents.utils import test_utils
label_desc: Use image node_id 1 to create a new DummyActorNet object from inherited base classes: network with example: obj = DummyActorNet(input_spec, action_spec, preprocessing_layers, name)