code: def train(env_id, num_timesteps, seed):
      """
      Train PPO2 model for Mujoco 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.
      """
  
      def make_env():
          env_out = gym.make(env_id)
          env_out = bench.Monitor(
              env_out, logger.get_dir(), allow_early_resets=True
          )
          return env_out
  
      env = DummyVecEnv([make_env])
      env = VecNormalize(env)
  
      set_global_seeds(seed)
      policy = MlpPolicy
      model = PPO2(
          policy=policy,
          env=env,
          n_steps=2048,
          nminibatches=32,
          lam=0.95,
          gamma=0.99,
          noptepochs=10,
          ent_coef=0.0,
          learning_rate=3e-4,
          cliprange=0.2,
      )
      model.learn(total_timesteps=num_timesteps)
  
      return model, env
  
file_path: stable-baselines/stable_baselines/ppo2/run_mujoco.py
imports: import numpy
  import gym
  from stable_baselines.common.cmd_util import mujoco_arg_parser
  from stable_baselines import bench, logger
  from stable_baselines.common import set_global_seeds
  from stable_baselines.common.vec_env.vec_normalize import VecNormalize
  from stable_baselines.ppo2 import PPO2
  from stable_baselines.common.policies import MlpPolicy
  from stable_baselines.common.vec_env.dummy_vec_env import DummyVecEnv
label_desc: Use image node_id 1 for calling a global function with example usage: train(env_id, num_timesteps, seed) and returns: model,  env, env_out