code: def test_generate_vec_env_non_image_observation():
      env = DummyVecEnv([lambda: gym.make("CartPole-v1")] * 2)
  
      model = PPO2("MlpPolicy", env)
      model.learn(total_timesteps=300)
  
      generate_expert_traj(
          model, save_path=".", n_timesteps=0, n_episodes=5
      )
  
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 10 for calling a global function with example usage: test_generate_vec_env_non_image_observation() without return types