code: def train(env_id, num_timesteps, seed):
      """
      Train TRPO model for the atari environment, for testing purposes
  
      :param env_id: (str) Environment ID
      :param num_timesteps: (int) The total number of samples
      :param seed: (int) The initial seed for training
      """
      rank = MPI.COMM_WORLD.Get_rank()
  
      if rank == 0:
          logger.configure()
      else:
          logger.configure(format_strs=[])
  
      workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
      set_global_seeds(workerseed)
      env = make_atari(env_id)
  
      env = bench.Monitor(
          env,
          logger.get_dir()
          and os.path.join(logger.get_dir(), str(rank)),
      )
      env.seed(workerseed)
  
      env = wrap_deepmind(env)
      env.seed(workerseed)
  
      model = TRPO(
          CnnPolicy,
          env,
          timesteps_per_batch=512,
          max_kl=0.001,
          cg_iters=10,
          cg_damping=1e-3,
          entcoeff=0.0,
          gamma=0.98,
          lam=1,
          vf_iters=3,
          vf_stepsize=1e-4,
      )
      model.learn(total_timesteps=int(num_timesteps * 1.1))
      env.close()
      # Free memory
      del env
  
file_path: stable-baselines/stable_baselines/trpo_mpi/run_atari.py
imports: import os
  from mpi4py import MPI
  from stable_baselines.common import set_global_seeds
  from stable_baselines import bench, logger, TRPO
  from stable_baselines.common.atari_wrappers import make_atari, wrap_deepmind
  from stable_baselines.common.cmd_util import atari_arg_parser
  from stable_baselines.common.policies import CnnPolicy
label_desc: Use image node_id 1 for calling a global function with example usage: train(env_id, num_timesteps, seed) without return types