code: def test_pretrain_twice(tmp_path):
      """
      Test pretraining twice in the same execution.
      """
      dataset = ExpertDataset(
          expert_path=EXPERT_PATH_PENDULUM,
          traj_limitation=10,
          sequential_preprocessing=True,
          verbose=0,
      )
      model = PPO2("MlpPolicy", "Pendulum-v0")
      model.pretrain(dataset, n_epochs=5)
      model.pretrain(dataset, n_epochs=5)
      del dataset, model
  
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 4 for calling a global function with example usage: test_pretrain_twice(tmp_path) without return types