code: def train(env_id, num_timesteps, seed):
      from baselines.ppo1 import mlp_policy, pposgd_simple
  
      U.make_session(num_cpu=1).__enter__()
  
      def policy_fn(name, ob_space, ac_space):
          return mlp_policy.MlpPolicy(
              name=name,
              ob_space=ob_space,
              ac_space=ac_space,
              hid_size=64,
              num_hid_layers=2,
          )
  
      env = make_mujoco_env(env_id, seed)
      pposgd_simple.learn(
          env,
          policy_fn,
          max_timesteps=num_timesteps,
          timesteps_per_actorbatch=2048,
          clip_param=0.2,
          entcoeff=0.0,
          optim_epochs=10,
          optim_stepsize=3e-4,
          optim_batchsize=64,
          gamma=0.99,
          lam=0.95,
          schedule="linear",
      )
      env.close()
  
imports: from baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser
  from baselines.common import tf_util
  from baselines import logger
label_desc: Use image node_id 1 for calling a global function with example usage: train(env_id, num_timesteps, seed) and returns: mlp_policy