code: def train(
      env_id,
      num_timesteps,
      seed,
      policy,
      n_envs=8,
      nminibatches=4,
      n_steps=128,
  ):
      """
      Train PPO2 model for atari environment, for testing purposes
  
      :param env_id: (str) the environment id string
      :param num_timesteps: (int) the number of timesteps to run
      :param seed: (int) Used to seed the random generator.
      :param policy: (Object) The policy model to use (MLP, CNN, LSTM, ...)
      :param n_envs: (int) Number of parallel environments
      :param nminibatches: (int) Number of training minibatches per update. For recurrent policies,
          the number of environments run in parallel should be a multiple of nminibatches.
      :param n_steps: (int) The number of steps to run for each environment per update
          (i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
      """
  
      env = VecFrameStack(make_atari_env(env_id, n_envs, seed), 4)
      policy = {
          "cnn": CnnPolicy,
          "lstm": CnnLstmPolicy,
          "lnlstm": CnnLnLstmPolicy,
          "mlp": MlpPolicy,
      }[policy]
      model = PPO2(
          policy=policy,
          env=env,
          n_steps=n_steps,
          nminibatches=nminibatches,
          lam=0.95,
          gamma=0.99,
          noptepochs=4,
          ent_coef=0.01,
          learning_rate=lambda f: f * 2.5e-4,
          cliprange=lambda f: f * 0.1,
          verbose=1,
      )
      model.learn(total_timesteps=num_timesteps)
  
      env.close()
      # Free memory
      del model
  
imports: from stable_baselines import PPO2, logger
  from stable_baselines.common.cmd_util import make_atari_env, atari_arg_parser
  from stable_baselines.common.vec_env import VecFrameStack
  from stable_baselines.common.policies import CnnPolicy, CnnLstmPolicy, CnnLnLstmPolicy, MlpPolicy
label_desc: Use image node_id 1 for calling a global function with example usage: train(env_id, num_timesteps, seed, policy, n_envs, nminibatches, n_steps) without return types