code: def setup_distributed(self, cluster_config, protocol="grpc"):
      self.cluster_config = cluster_config
  
      ps_hosts = self.cluster_config["ps"]
      chief_hosts = self.cluster_config["chief"]
      worker_hosts = self.cluster_config["worker"]
      if chief_hosts:
          worker_hosts = chief_hosts + worker_hosts
  
      task_type = self.task_type
      if task_type == "worker" and chief_hosts:
          self.task_index += 1
  
      if self.task_type == "chief":
          task_type = "worker"
          # Make sure ps is launched earlier than the chief
          # TODO: any better way to do this?
          time.sleep(2)
  
      print("ps hosts: ", ps_hosts)
      print("worker hosts: ", worker_hosts)
      print("task type: ", task_type)
      print("task index: ", self.task_index)
      cluster = tf.train.ClusterSpec(
          {"ps": ps_hosts, "worker": worker_hosts}
      )
      self.server = tf.distribute.Server(
          cluster,
          job_name=task_type,
          task_index=self.task_index,
          protocol=protocol,
      )
      if self.task_type == "ps":
          print("Launching parameter server")
          self.server.join()
      else:
          print("Launching worker")
          self.config["task_index"] = self.task_index
          self.tf_config = {
              "ps_hosts": ps_hosts,
              "worker_hosts": worker_hosts,
              "type": task_type,
              "index": self.task_index,
              "is_chief": self.task_type == "chief",
          }
          tf_device = tf.device(
              tf.train.replica_device_setter(
                  worker_device="/job:worker/task:%d" % self.task_index,
                  cluster=cluster,
              )
          )
      return self.task_type
  
imports: import time
  import argparse
  import tensorflow
  import os
  import sys
  import math
  import collections
  from tensorflow.python.client import timeline
  import json
  from tensorflow.python.ops import partitioned_variables
  import ray
  from bigdl.dllib.utils.log4Error import *
  from bigdl.orca import init_orca_context, stop_orca_context, OrcaContext
  from bigdl.orca.data.utils import process_spark_xshards
  from bigdl.orca.learn.utils import maybe_dataframe_to_xshards
  from bigdl.friesian.feature import FeatureTable
label_desc: Use image node_id 4 for calling the RayWorker obj's underlying member method code with example usage: obj.setup_distributed(cluster_config, protocol) and returns: self