code: def local_vectorized():
      """
      Train agent on experience collected in parallel from one vectorized CartPole environment.
  
      Typical use case:
          time for vectorized environment < time for sequential execution
      """
      agent = "benchmarks/configs/ppo.json"
      environment = "custom_cartpole"
      runner = Runner(
          agent=agent,
          environment=environment,
          max_episode_timesteps=500,
          num_parallel=4,
      )
      runner.run(num_episodes=100)
      runner.close()
  
imports: from threading import Thread
  from tensorforce import Environment, Runner
label_desc: Use image node_id 3 for calling a global function with example usage: local_vectorized() without return types