code: def init_process_group(
      init_method: Optional[str],
      world_size: int,
      rank: Rank,
      groups: List[List[Rank]],
      backend: str = "gloo",
  ) -> List["td.ProcessGroup"]:
      # With the THD backend there were no timeouts so high variance in
      # execution time between trainers was not a problem. With the new c10d
      # implementation we do have to take timeouts into account. To simulate
      # the old behavior we use a ridiculously high default timeout.
      timeout = timedelta(days=365)
      logger.info("init_process_group start")
  
      # Adding code block below to route ftw region traffic to new
      # ensembles - T132536412; Easy to extend to other regions and
      # handle more traffics by adding more ensembles
      try:
          # fetch run time trainer cluster which contains region information
          runtime_cluster = os.environ["BUMBLEBEE_CLUSTER"]
          cluster_region = runtime_cluster.split("-")[1]
          filamentZeusMap = {"ftw": ("zelos.8fc7", "zelos.5f14")}
          if cluster_region in filamentZeusMap:
              logger.info(
                  f"Run time cluster region is: {cluster_region}"
              )
              regionList = filamentZeusMap[cluster_region]
              parent_flow_id = init_method.split("f")[-1]
              zeus_endpoint = regionList[
                  int(parent_flow_id) % len(regionList)
              ]
              init_method = (
                  f"elasticzeus://{zeus_endpoint}/f{parent_flow_id}"
              )
              logger.info(
                  "The updated init_method: {}".format(init_method)
              )
          else:
              logger.info(
                  "Run time cluster region not in Filament ensemble map"
              )
  
      except Exception as e:
          logger.info(f"List BUMBLEBEE_CLUSTER test FAILED due to: {e}")
  
      if init_method is None:
          raise RuntimeError(
              "distributed_init_method must be set when num_machines > 1"
          )
      td.init_process_group(
          backend,
          init_method=init_method,
          world_size=world_size,
          rank=rank,
          timeout=timeout,
      )
      logger.info("init_process_group creating groups")
      group_objs = []
      for group in groups:
          group_objs.append(td.new_group(group, timeout=timeout))
      logger.info("init_process_group done")
      return group_objs
  
file_path: PyTorch-BigGraph/torchbiggraph/distributed.py
imports: import logging
  import multiprocessing
  import os
  from abc import ABC, abstractmethod
  from datetime import timedelta
  from typing import Callable, List, NamedTuple, Optional
  import torch.distributed
  import torch.multiprocessing
  from torchbiggraph.types import Rank
  from torchbiggraph.util import tag_logs_with_process_name
label_desc: Use image node_id 1 for calling a global function with example usage: init_process_group(init_method, world_size, rank, groups, backend) and returns: group_objs