code: def test_generate_callable(tmp_path):
      """
      Test generating expert trajectories with a callable.
      """
      env = gym.make("CartPole-v1")
  
      # Here the expert is a random agent
      def dummy_expert(_obs):
          return env.action_space.sample()
  
      generate_expert_traj(
          dummy_expert,
          tmp_path / "dummy_expert_cartpole",
          env,
          n_timesteps=0,
          n_episodes=10,
      )
  
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 3 for calling a global function with example usage: test_generate_callable(tmp_path) and returns: env