code: def train(env_id, num_timesteps, seed):
      """
      Train PPO1 model for Robotics 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()
      with mujoco_py.ignore_mujoco_warnings():
          workerseed = seed + 10000 * rank
          set_global_seeds(workerseed)
          env = make_robotics_env(env_id, workerseed, rank=rank)
  
          model = PPO1(
              MlpPolicy,
              env,
              timesteps_per_actorbatch=2048,
              clip_param=0.2,
              entcoeff=0.0,
              optim_epochs=5,
              optim_stepsize=3e-4,
              optim_batchsize=256,
              gamma=0.99,
              lam=0.95,
              schedule="linear",
          )
          model.learn(total_timesteps=num_timesteps)
          env.close()
  
imports: from mpi4py import MPI
  import mujoco_py
  from stable_baselines.common import set_global_seeds
  from stable_baselines.common.policies import MlpPolicy
  from stable_baselines.common.cmd_util import make_robotics_env, robotics_arg_parser
  from stable_baselines.ppo1 import PPO1
label_desc: Use image node_id 1 for calling a global function with example usage: train(env_id, num_timesteps, seed) without return types