code: def train(env_id, num_timesteps, seed, num_cpu):
      """
      train an ACKTR model on atari
  
      :param env_id: (str) Environment ID
      :param num_timesteps: (int) The total number of samples
      :param seed: (int) The initial seed for training
      :param num_cpu: (int) The number of cpu to train on
      """
      env = VecFrameStack(make_atari_env(env_id, num_cpu, seed), 4)
      model = ACKTR(CnnPolicy, env, nprocs=num_cpu, seed=seed)
      model.learn(total_timesteps=int(num_timesteps * 1.1))
      env.close()
  
imports: from stable_baselines import logger, ACKTR
  from stable_baselines.common.cmd_util import make_atari_env, atari_arg_parser
  from stable_baselines.common.vec_env.vec_frame_stack import VecFrameStack
  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, num_cpu) without return types