class_docstr: This should be a pickable class.
class_docstr_tok: ["This","should","be","a","pickable","class","."]
code: def gen_ray_start(self, master_ip):
      def _start_ray_services(iter):
          from pyspark import BarrierTaskContext
          from zoo.util.utils import get_node_ip
  
          tc = BarrierTaskContext.get()
          current_ip = get_node_ip()
          print("current address {}".format(current_ip))
          print("master address {}".format(master_ip))
          redis_address = "{}:{}".format(master_ip, self.redis_port)
          process_info = None
          base_path = tempfile.gettempdir()
          ray_master_flag_path = os.path.join(
              base_path, self.ray_master_flag
          )
          if current_ip == master_ip:  # Start the ray master.
              # It is possible that multiple executors are on one node. In this case,
              # the first executor that gets the lock would be the master and it would
              # create a flag to indicate the master has initialized.
              # The flag file is removed when ray start processes finish so that this
              # won't affect other programs.
              ray_master_lock_path = os.path.join(
                  base_path, self.ray_master_lock
              )
              with filelock.FileLock(ray_master_lock_path):
                  if not os.path.exists(ray_master_flag_path):
                      print(
                          "partition id is : {}".format(
                              tc.partitionId()
                          )
                      )
                      process_info = self._start_ray_node(
                          command=self._gen_master_command(),
                          tag="ray-master",
                      )
                      process_info.master_addr = redis_address
                      os.mknod(ray_master_flag_path)
  
          tc.barrier()
          if not process_info:  # Start raylets.
              # Add a lock to avoid starting multiple raylets on one node at the same time.
              # See this issue: https://github.com/ray-project/ray/issues/10154
              raylet_lock_path = os.path.join(
                  base_path, self.raylet_lock
              )
              with filelock.FileLock(raylet_lock_path):
                  print("partition id is : {}".format(tc.partitionId()))
                  process_info = self._start_ray_node(
                      command=RayServiceFuncGenerator._get_raylet_command(
                          redis_address=redis_address,
                          ray_exec=self.ray_exec,
                          redis_password=self.redis_password,
                          ray_node_cpu_cores=self.ray_node_cpu_cores,
                          labels=self.labels,
                          object_store_memory=self.object_store_memory,
                          extra_params=self.extra_params,
                      ),
                      tag="raylet",
                  )
                  kill_redundant_log_monitors(
                      redis_address=redis_address
                  )
  
          if os.path.exists(ray_master_flag_path):
              os.remove(ray_master_flag_path)
          yield process_info
  
      return _start_ray_services
  
imports: import os
  import re
  import subprocess
  import time
  import uuid
  import random
  import warnings
  import tempfile
  import filelock
  import multiprocessing
  from packaging import version
  from zoo.ray.process import session_execute, ProcessMonitor
  from zoo.ray.utils import is_local
  from zoo.ray.utils import resource_to_bytes
  from zoo.ray.utils import get_parent_pid
label_desc: Use image node_id 13 for calling the RayServiceFuncGenerator obj's underlying member method code with example usage: obj.gen_ray_start(master_ip) and returns: _start_ray_services