code: def test_replay_recorded_actions_policy():
      """unit tests"""
  
      teacher_policy = HeuristicLunarLanderPolicy()
  
      env = GymMazeEnv("LunarLander-v2")
      env = ActionRecordingWrapper.wrap(
          env,
          record_maze_actions=False,
          record_actions=True,
          output_dir="action_records",
      )
  
      env.seed(1234)
      obs = env.reset()
      done = False
      cum_reward_teacher = 0
      while not done:
          action = teacher_policy.compute_action(obs)
          obs, rew, done, info = env.step(action)
          cum_reward_teacher += rew
  
      env.dump()
  
      episode_id = env.get_episode_id()
      expected_file_path = "action_records/" + str(episode_id) + ".pkl"
      replay_policy = ReplayRecordedActionsPolicy(
          action_record_path=expected_file_path, with_agent_actions=True
      )
  
      env.seed(1234)
      obs = env.reset()
      done = False
      cum_reward_replay = 0
      while not done:
          action = replay_policy.compute_action(
              obs, maze_state=env.get_maze_state(), env=env
          )
          obs, rew, done, info = env.step(action)
          cum_reward_replay += rew
  
      assert cum_reward_teacher == cum_reward_replay
      assert (
          cum_reward_replay
          == replay_policy.action_record.cum_action_record_reward
      )
  
imports: from maze.core.agent.heuristic_lunar_lander_policy import HeuristicLunarLanderPolicy
  from maze.core.agent.replay_recorded_actions_policy import ReplayRecordedActionsPolicy
  from maze.core.wrappers.action_recording_wrapper import ActionRecordingWrapper
  from maze.core.wrappers.maze_gym_env_wrapper import GymMazeEnv
label_desc: Use image node_id 1 for calling a global function with example usage: test_replay_recorded_actions_policy() without return types