code: def training_example(num_episodes: int, max_episode_timesteps: int):
      # Instantiate the environment (run the CARLA simulator before doing this!)
      env = CARLAEnvironment(debug=True)
  
      # Create your own agent (here is just an example)
      agent = Agent.create(
          agent="ppo",
          environment=env,
          max_episode_timesteps=max_episode_timesteps,
          batch_size=1,
      )
  
      # Training loop (you couldn't use a Runner instead)
      # `weights_dir` and `record_dir` are `None` to prevent saving and recording
      env.train(
          agent=agent,
          num_episodes=num_episodes,
          max_episode_timesteps=max_episode_timesteps,
          weights_dir=None,
          record_dir=None,
      )
  
      pygame.quit()
  
imports: import pygame
  from tensorforce import Agent
  from tensorforce.environments import CARLAEnvironment
label_desc: Use image node_id 1 for calling a global function with example usage: training_example(num_episodes, max_episode_timesteps) without return types