class_docstr: Backend worker to handle Model Server's python service code
docstr: Run the backend worker process and listen on a socket
  :return:
docstr_tok: ["Run","the","backend","worker","process","and","listen","on","a","socket",":","return",":"]
code: def run_server(self):
      """
      Run the backend worker process and listen on a socket
      :return:
      """
      if not DEBUG:
          self.sock.settimeout(SOCKET_ACCEPT_TIMEOUT)
  
      self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
  
      if self.sock_type == "unix":
          self.sock.bind(self.sock_name)
      else:
          self.sock.bind((self.sock_name, int(self.port)))
  
      self.sock.listen(1)
  
      logging.info("[PID]%d", os.getpid())
      logging.info("Torch worker started.")
      logging.info("Python runtime: %s", platform.python_version())
  
      while True:
          (cl_socket, _) = self.sock.accept()
          # workaround error(35, 'Resource temporarily unavailable') on OSX
          cl_socket.setblocking(True)
  
          logging.info(
              "Connection accepted: %s.", cl_socket.getsockname()
          )
          self.handle_connection(cl_socket)
  
file_path: serve/ts/model_service_worker.py
imports: import logging
  import os
  import platform
  import socket
  import sys
  from typing import Optional
  from ts.arg_parser import ArgParser
  from ts.metrics.metric_cache_yaml_impl import MetricsCacheYamlImpl
  from ts.model_loader import ModelLoaderFactory
  from ts.protocol.otf_message_handler import create_load_model_response, retrieve_msg
label_desc: Use image node_id 4 for calling the TorchModelServiceWorker obj's underlying member method code with example usage: obj.run_server() without return types