code: def __init__(self):
      self.rpm = ReplayMemory(int(2e6), OBS_DIM, ACT_DIM)
  
      # Need acquire lock when model learning or predicting
      self.locks = []
      for i in range(args.ensemble_num):
          self.locks.append(threading.Lock())
  
      models = []
      for i in range(args.ensemble_num):
          models.append(
              OpenSimModel(OBS_DIM, VEL_DIM, ACT_DIM, model_id=i)
          )
  
      hyperparas = {
          "gamma": GAMMA,
          "tau": TAU,
          "ensemble_num": args.ensemble_num,
      }
      alg = MultiHeadDDPG(models, hyperparas)
  
      self.agent = OpenSimAgent(
          alg, OBS_DIM, ACT_DIM, args.ensemble_num
      )
  
      self.scalars_manager = ScalarsManager(logger.get_dir())
  
      # add lock when appending data to rpm or writing scalars to tensorboard
      self.MEMORY_LOCK = threading.Lock()
  
      self.clients = defaultdict(self.ClientState)
  
      self.ready_client_queue = queue.Queue()
  
      self.noiselevel = 0.5
      self.global_step = 0
  
      # thread to keep training
      t = threading.Thread(target=self.keep_training)
      t.start()
  
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 1 to create a new SimulatorServer object from inherited base classes: simulator_pb2_grpc with example: obj = SimulatorServer()