code: def multi_init(opt, device_id):
      dist_init_method = "tcp://{master_ip}:{master_port}".format(
          master_ip=opt.master_ip, master_port=opt.master_port
      )
      dist_world_size = opt.world_size
      torch.distributed.init_process_group(
          backend=opt.gpu_backend,
          init_method=dist_init_method,
          world_size=dist_world_size,
          rank=opt.gpu_ranks[device_id],
          timeout=timedelta(seconds=opt.timeout),
      )
      gpu_rank = torch.distributed.get_rank()
      if not is_master(opt, device_id):
          logger.disabled = True
  
      return gpu_rank
  
imports: import os
  import signal
  import math
  import pickle
  import torch.distributed
  from datetime import timedelta
  from onmt.translate.translator import build_translator
  from onmt.transforms import get_transforms_cls
  from onmt.constants import CorpusTask
  from onmt.utils.logging import init_logger, logger
  from onmt.inputters.dynamic_iterator import build_dynamic_dataset_iter
label_desc: Use image node_id 2 for calling a global function with example usage: multi_init(opt, device_id) and returns: gpu_rank