code: def test_behavior_cloning_box(tmp_path, model_class):
      """
      Behavior cloning with continuous actions.
      """
      dataset = ExpertDataset(
          expert_path=EXPERT_PATH_PENDULUM,
          traj_limitation=10,
          sequential_preprocessing=True,
          verbose=0,
      )
      model = model_class("MlpPolicy", "Pendulum-v0")
      model.pretrain(dataset, n_epochs=5)
      model.save(str(tmp_path / "test-pretrain"))
      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 7 for calling a global function with example usage: test_behavior_cloning_box(tmp_path, model_class) without return types