code: def train(env_id, num_timesteps, seed, policy, lr_schedule, num_env):
      """
      Train A2C model for 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
      :param policy: (A2CPolicy) The policy model to use (MLP, CNN, LSTM, ...)
      :param lr_schedule: (str) The type of scheduler for the learning rate update ('linear', 'constant',
                                   'double_linear_con', 'middle_drop' or 'double_middle_drop')
      :param num_env: (int) The number of environments
      """
      policy_fn = None
      if policy == "cnn":
          policy_fn = CnnPolicy
      elif policy == "lstm":
          policy_fn = CnnLstmPolicy
      elif policy == "lnlstm":
          policy_fn = CnnLnLstmPolicy
      if policy_fn is None:
          raise ValueError(
              "Error: policy {} not implemented".format(policy)
          )
  
      env = VecFrameStack(make_atari_env(env_id, num_env, seed), 4)
  
      model = A2C(policy_fn, env, lr_schedule=lr_schedule, seed=seed)
      model.learn(total_timesteps=int(num_timesteps * 1.1))
      env.close()
  
imports: from stable_baselines import logger, A2C
  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
label_desc: Use image node_id 1 for calling a global function with example usage: train(env_id, num_timesteps, seed, policy, lr_schedule, num_env) without return types