code: def train(env_id, num_timesteps, seed):
      """
      Train TRPO model for the mujoco 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
      """
      with tf_util.single_threaded_session():
          rank = MPI.COMM_WORLD.Get_rank()
          if rank == 0:
              logger.configure()
          else:
              logger.configure(format_strs=[])
              logger.set_level(logger.DISABLED)
          workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
  
          env = make_mujoco_env(env_id, workerseed)
          model = TRPO(
              MlpPolicy,
              env,
              timesteps_per_batch=1024,
              max_kl=0.01,
              cg_iters=10,
              cg_damping=0.1,
              entcoeff=0.0,
              gamma=0.99,
              lam=0.98,
              vf_iters=5,
              vf_stepsize=1e-3,
          )
          model.learn(total_timesteps=num_timesteps)
          env.close()
  
imports: from mpi4py import MPI
  from stable_baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser
  from stable_baselines.common.policies import MlpPolicy
  from stable_baselines import logger
  from stable_baselines.trpo_mpi import TRPO
  import stable_baselines.common.tf_util
label_desc: Use image node_id 1 for calling a global function with example usage: train(env_id, num_timesteps, seed) without return types