code: def get_actor_thread(
      test_case, reverb_server_port, num_iterations=10
  ):
      """Returns a thread that runs an Actor."""
  
      def build_and_run_actor():
          root_dir = test_case.create_tempdir().full_path
          (
              env,
              action_tensor_spec,
              time_step_tensor_spec,
          ) = get_cartpole_env_and_specs()
  
          train_step = train_utils.create_train_step()
  
          q_net = build_dummy_sequential_net(
              fc_layer_params=(100,), action_spec=action_tensor_spec
          )
  
          agent = dqn_agent.DqnAgent(
              time_step_tensor_spec,
              action_tensor_spec,
              q_network=q_net,
              optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
              train_step_counter=train_step,
          )
  
          (
              _,
              rb_observer,
          ) = replay_buffer_utils.get_reverb_buffer_and_observer(
              agent.collect_data_spec,
              table_name=reverb_replay_buffer.DEFAULT_TABLE,
              sequence_length=2,
              reverb_server_address="localhost:{}".format(
                  reverb_server_port
              ),
          )
  
          variable_container = (
              reverb_variable_container.ReverbVariableContainer(
                  server_address="localhost:{}".format(
                      reverb_server_port
                  ),
                  table_names=[reverb_variable_container.DEFAULT_TABLE],
              )
          )
  
          test_actor = build_actor(
              root_dir, env, agent, rb_observer, train_step
          )
  
          variables_dict = {
              reverb_variable_container.POLICY_KEY: agent.collect_policy.variables(),
              reverb_variable_container.TRAIN_STEP_KEY: train_step,
          }
          variable_container.update(variables_dict)
  
          for _ in range(num_iterations):
              test_actor.run()
  
      actor_thread = test_case.checkedThread(target=build_and_run_actor)
      return actor_thread
  
imports: import functools
  from absl import logging
  import numpy
  import reverb
  import tensorflow
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.agents.ppo import ppo_clip_agent
  from tf_agents.environments import suite_gym
  from tf_agents.experimental.distributed import reverb_variable_container
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import sequential
  from tf_agents.networks import value_network
  from tf_agents.policies import py_tf_eager_policy
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.specs import tensor_spec
  from tf_agents.train import actor
  from tf_agents.train.utils import replay_buffer_utils
  from tf_agents.train.utils import spec_utils
  from tf_agents.train.utils import train_utils
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
label_desc: Use image node_id 7 for calling a global function with example usage: get_actor_thread(test_case, reverb_server_port, num_iterations) and returns: actor_thread