code: def test_pretrain_images(tmp_path):
      env = make_atari_env("PongNoFrameskip-v4", num_env=1, seed=0)
      env = VecFrameStack(env, n_stack=3)
      model = PPO2("CnnPolicy", env)
      generate_expert_traj(
          model,
          str(tmp_path / "expert_pong"),
          n_timesteps=0,
          n_episodes=1,
          image_folder=str(tmp_path / "pretrain_recorded_images"),
      )
  
      expert_path = str(tmp_path / "expert_pong.npz")
      dataset = ExpertDataset(
          expert_path=expert_path,
          traj_limitation=1,
          batch_size=32,
          sequential_preprocessing=True,
      )
      model.pretrain(dataset, n_epochs=2)
  
      shutil.rmtree(str(tmp_path / "pretrain_recorded_images"))
      env.close()
      del dataset, model, env
  
imports: import os
  import shutil
  import gym
  import numpy
  import pytest
  from stable_baselines import A2C, ACER, ACKTR, GAIL, DDPG, DQN, PPO1, PPO2, TD3, TRPO, SAC
  from stable_baselines.common.cmd_util import make_atari_env
  from stable_baselines.common.vec_env import VecFrameStack, DummyVecEnv
  from stable_baselines.common.evaluation import evaluate_policy
  from stable_baselines.common.callbacks import CheckpointCallback
  from stable_baselines.gail import ExpertDataset, generate_expert_traj
label_desc: Use image node_id 5 for calling a global function with example usage: test_pretrain_images(tmp_path) without return types