code: def handle_distributed_coach_orchestrator(args):
      from rl_coach.orchestrators.kubernetes_orchestrator import (
          KubernetesParameters,
          Kubernetes,
          RunTypeParameters,
      )
  
      ckpt_inside_container = "/checkpoint"
      arg_list = sys.argv[1:]
      try:
          i = arg_list.index("--distributed_coach_run_type")
          arg_list.pop(i)
          arg_list.pop(i)
      except ValueError:
          pass
  
      trainer_command = [
          "python3",
          "rl_coach/coach.py",
          "--distributed_coach_run_type",
          str(RunType.TRAINER),
      ] + arg_list
      rollout_command = [
          "python3",
          "rl_coach/coach.py",
          "--distributed_coach_run_type",
          str(RunType.ROLLOUT_WORKER),
      ] + arg_list
  
      if "--experiment_name" not in rollout_command:
          rollout_command = rollout_command + [
              "--experiment_name",
              args.experiment_name,
          ]
  
      if "--experiment_name" not in trainer_command:
          trainer_command = trainer_command + [
              "--experiment_name",
              args.experiment_name,
          ]
  
      memory_backend_params = None
      if args.memory_backend == "redispubsub":
          memory_backend_params = RedisPubSubMemoryBackendParameters()
  
      ds_params_instance = None
      if args.data_store == "s3":
          ds_params = DataStoreParameters("s3", "", "")
          ds_params_instance = S3DataStoreParameters(
              ds_params=ds_params,
              end_point=args.s3_end_point,
              bucket_name=args.s3_bucket_name,
              creds_file=args.s3_creds_file,
              checkpoint_dir=ckpt_inside_container,
              expt_dir=args.experiment_path,
          )
      elif args.data_store == "nfs":
          ds_params = DataStoreParameters("nfs", "kubernetes", "")
          ds_params_instance = NFSDataStoreParameters(ds_params)
      elif args.data_store == "redis":
          ds_params = DataStoreParameters("redis", "kubernetes", "")
          ds_params_instance = RedisDataStoreParameters(ds_params)
      else:
          raise ValueError(
              "data_store {} found. Expected 's3' or 'nfs'".format(
                  args.data_store
              )
          )
  
      worker_run_type_params = RunTypeParameters(
          args.image,
          rollout_command,
          run_type=str(RunType.ROLLOUT_WORKER),
          num_replicas=args.num_workers,
      )
      trainer_run_type_params = RunTypeParameters(
          args.image, trainer_command, run_type=str(RunType.TRAINER)
      )
  
      orchestration_params = KubernetesParameters(
          [worker_run_type_params, trainer_run_type_params],
          kubeconfig="~/.kube/config",
          memory_backend_parameters=memory_backend_params,
          data_store_params=ds_params_instance,
      )
      orchestrator = Kubernetes(orchestration_params)
      if not orchestrator.setup(args.checkpoint_restore_dir):
          screen.print("Could not setup.")
          return 1
  
      if orchestrator.deploy_trainer():
          screen.print("Successfully deployed trainer.")
      else:
          screen.print("Could not deploy trainer.")
          return 1
  
      if orchestrator.deploy_worker():
          screen.print("Successfully deployed rollout worker(s).")
      else:
          screen.print("Could not deploy rollout worker(s).")
          return 1
  
      if args.dump_worker_logs:
          screen.log_title(
              "Dumping rollout worker logs in: {}".format(
                  args.experiment_path
              )
          )
          orchestrator.worker_logs(path=args.experiment_path)
  
      exit_code = 1
      try:
          exit_code = orchestrator.trainer_logs()
      except KeyboardInterrupt:
          pass
  
      orchestrator.undeploy()
      return exit_code
  
imports: import sys
  import copy
  from configparser import ConfigParser, Error
  import os
  from rl_coach import logger
  import traceback
  from rl_coach.logger import screen, failed_imports
  import argparse
  import atexit
  import time
  import sys
  import json
  from rl_coach.base_parameters import Frameworks, VisualizationParameters, TaskParameters, DistributedTaskParameters, RunType, DistributedCoachSynchronizationType
  from rl_coach.core_types import TotalStepsCounter, RunPhase, PlayingStepsType, TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, StepMethod, Transition
  from multiprocessing import Process
  from multiprocessing.managers import BaseManager
  import subprocess
  from glob import glob
  from rl_coach.graph_managers.graph_manager import HumanPlayScheduleParameters, GraphManager
  from rl_coach.utils import list_all_presets, short_dynamic_import, get_open_port, SharedMemoryScratchPad, get_base_dir, set_gpu
  from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
  from rl_coach.environments.environment import SingleLevelSelection
  from rl_coach.memories.backend.redis import RedisPubSubMemoryBackendParameters
  from rl_coach.memories.backend.memory_impl import construct_memory_params
  from rl_coach.data_stores.data_store import DataStoreParameters
  from rl_coach.data_stores.s3_data_store import S3DataStoreParameters
  from rl_coach.data_stores.nfs_data_store import NFSDataStoreParameters
  from rl_coach.data_stores.redis_data_store import RedisDataStoreParameters
  from rl_coach.data_stores.data_store_impl import get_data_store, construct_data_store_params
  from rl_coach.training_worker import training_worker
  from rl_coach.rollout_worker import rollout_worker
  from rl_coach.schedules import *
  from rl_coach.exploration_policies.e_greedy import *
label_desc: Use image node_id 6 for calling a global function with example usage: handle_distributed_coach_orchestrator(args) and returns: exit_code, int, int, int