code: def _batch_policy_learn(self, obs, actions, advantages):
      if self.loss_type == "KLPEN":
          feed = {
              "obs": obs,
              "actions": actions,
              "advantages": advantages,
              "beta": self.beta,
          }
      else:
          feed = {
              "obs": obs,
              "actions": actions,
              "advantages": advantages,
          }
      [loss, kl] = self.fluid_executor.run(
          self.policy_learn_program,
          feed=feed,
          fetch_list=self.policy_learn_output,
      )
      return loss, kl
  
file_path: PARL/benchmark/fluid/PPO/mujoco_agent.py
imports: import numpy
  import parl
  from parl import layers
  from paddle import fluid
  from parl.utils import logger
label_desc: Use image node_id 6 for calling the MujocoAgent obj's underlying member method code with example usage: obj._batch_policy_learn(obs, actions, advantages) and returns: loss,  kl