code: def learn(self):
      start_time = time.time()
  
      for T in range(args.train_times):
          [
              states,
              actions,
              rewards,
              new_states,
              dones,
          ] = self.rpm.sample_batch(BATCH_SIZE)
          with self.model_lock:
              critic_loss = self.agent.learn(
                  states, actions, rewards, new_states, dones
              )
          self.critic_loss_stat.add(critic_loss)
      logger.info(
          "[learn] time consuming:{}".format(time.time() - start_time)
      )
  
imports: import os
  import parl
  import queue
  import six
  import threading
  import time
  import numpy
  from actor import Actor
  from opensim_model import OpenSimModel
  from opensim_agent import OpenSimAgent
  from parl.utils import logger, summary, get_gpu_count
  from replay_memory import ReplayMemory
  from parl.utils.window_stat import WindowStat
  from parl.remote.client import get_global_client
  from parl.utils import machine_info
label_desc: Use image node_id 6 for calling the Learner obj's underlying member method code with example usage: obj.learn() without return types