code: def train_single_model(self, model_idx, result_q):
      logger.info("[train_single_model] model_idx:{}".format(model_idx))
      critic_loss_list = []
      lock = self.locks[model_idx]
      memory = self.rpm
  
      actor_lr = ACTOR_LR * (1.0 - 0.05 * model_idx)
      critic_lr = CRITIC_LR * (1.0 + 0.1 * model_idx)
  
      for T in range(TRAIN_TIMES):
          [
              states,
              actions,
              rewards,
              new_states,
              dones,
          ] = memory.sample_batch(BATCH_SIZE)
          lock.acquire()
          critic_loss = self.agent.learn(
              states,
              actions,
              rewards,
              new_states,
              dones,
              actor_lr,
              critic_lr,
              model_idx,
          )
          lock.release()
          critic_loss_list.append(critic_loss)
      result_q.put(critic_loss_list)
  
imports: import grpc
  import json
  import numpy
  import os
  import queue
  import simulator_pb2
  import simulator_pb2_grpc
  import six
  import time
  import threading
  from args import get_server_args
  from collections import defaultdict
  from concurrent import futures
  from multi_head_ddpg import MultiHeadDDPG
  from opensim_agent import OpenSimAgent
  from opensim_model import OpenSimModel
  from parl.utils import logger
  from replay_memory import ReplayMemory
  from utils import calc_indicators, ScalarsManager, TransitionExperience
label_desc: Use image node_id 7 for calling the SimulatorServer obj's underlying member method code with example usage: obj.train_single_model(model_idx, result_q) without return types