code: def local():
      """
      Train agent on experience collected in parallel from 4 local CartPole environments.
  
      Typical use case:
          time for batched agent.act() ~ time for agent.act() > time for environment.execute()
      """
      agent = "benchmarks/configs/ppo.json"
      environment = "benchmarks/configs/cartpole.json"
      runner = Runner(
          agent=agent, environment=environment, num_parallel=4
      )
      # Batch act/observe calls to agent, unless environment.is_vectorizable()
      # (otherwise essentially equivalent to single environment)
      runner.run(num_episodes=100, batch_agent_calls=True)
      runner.close()
  
imports: from threading import Thread
  from tensorforce import Environment, Runner
label_desc: Use image node_id 2 for calling a global function with example usage: local() without return types