code: def __init__(
      self, time_step_spec, action_spec, actor_network, training=False
  ):
      del time_step_spec
      del actor_network
      del training
      single_action_spec = tf.nest.flatten(action_spec)[0]
      # Action is maximum of action range.
      self._action = single_action_spec.maximum
      self._action_spec = action_spec
      self.info_spec = ()
  
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 DummyActorPolicy object from inherited base classes: object with example: obj = DummyActorPolicy(time_step_spec, action_spec, actor_network, training)