code: def spawned_infer(
      opt, device_id, error_queue, queue_instruct, queue_result
  ):
      """Run various functions for translation in spawned process on `device_id`."""
      try:
          gpu_rank = multi_init(opt, device_id)
          if gpu_rank != opt.gpu_ranks[device_id]:
              raise AssertionError(
                  "An error occurred in \
                    Distributed initialization"
              )
          torch.cuda.set_device(device_id)
          init_logger(opt.log_file)
          translator = build_translator(
              opt, device_id, logger=logger, report_score=True
          )
          transforms_cls = get_transforms_cls(opt._all_transform)
          while True:
              instruction = queue_instruct.get()
              if instruction[0] == "stop":
                  break
              elif instruction[0] == "infer_list":
                  src = instruction[1]
                  infer_iter = build_dynamic_dataset_iter(
                      opt,
                      transforms_cls,
                      translator.vocabs,
                      task=CorpusTask.INFER,
                      src=src,
                      device_id=device_id,
                  )
                  scores, preds = translator._translate(
                      infer_iter,
                      infer_iter.transforms,
                      opt.attn_debug,
                      opt.align_debug,
                  )
                  queue_result.put(scores)
                  queue_result.put(preds)
              elif instruction[0] == "infer_file":
                  opt.src = instruction[1].src
                  infer_iter = build_dynamic_dataset_iter(
                      opt,
                      transforms_cls,
                      translator.vocabs,
                      task=CorpusTask.INFER,
                      device_id=device_id,
                  )
                  scores, preds = translator._translate(
                      infer_iter,
                      infer_iter.transforms,
                      opt.attn_debug,
                      opt.align_debug,
                  )
                  queue_result.put(scores)
                  queue_result.put(preds)
              elif instruction[0] == "score_list":
                  tgt = instruction[1]
                  infer_iter = build_dynamic_dataset_iter(
                      opt,
                      transforms_cls,
                      translator.vocabs,
                      task=CorpusTask.INFER,
                      src=tgt,
                      tgt=tgt,
                      device_id=device_id,
                  )
                  score_results = translator._score(infer_iter)
                  queue_result.put(score_results)
              elif instruction[0] == "score_file":
                  opt.src = instruction[1].src
                  opt.tgt = instruction[1].src
                  infer_iter = build_dynamic_dataset_iter(
                      opt,
                      transforms_cls,
                      translator.vocabs,
                      task=CorpusTask.INFER,
                      device_id=device_id,
                  )
                  score_results = translator._score(infer_iter)
                  queue_result.put(score_results)
  
      except KeyboardInterrupt:
          pass  # killed by parent, do nothing
      except Exception:
          # propagate exception to parent process, keeping original traceback
          import traceback
  
          error_queue.put(
              (opt.gpu_ranks[device_id], traceback.format_exc())
          )
  
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 6 for calling a global function with example usage: spawned_infer(opt, device_id, error_queue, queue_instruct, queue_result) without return types