code: def create_workers(
      devices: List[torch.device],
  ) -> Tuple[List[InQueue], List[OutQueue]]:
      """Spawns worker threads. A worker thread is bound to a device."""
      in_queues: List[InQueue] = []
      out_queues: List[OutQueue] = []
  
      # Spawn workers.
      workers: Dict[torch.device, Tuple[InQueue, OutQueue]] = {}
  
      def normalize_device(device: torch.device) -> torch.device:
          if device.type == "cuda" and device.index is None:
              return torch.device(
                  "cuda", index=torch.cuda.current_device()
              )
  
          if device.type == "cpu" and device.index is not None:
              return torch.device("cpu")
  
          return device
  
      for device in devices:
          device = normalize_device(device)
  
          try:
              in_queue, out_queue = workers[device]
          except KeyError:
              in_queue = Queue()
              out_queue = Queue()
              workers[device] = (in_queue, out_queue)
  
              t = Thread(
                  target=worker,
                  args=(in_queue, out_queue, device),
                  daemon=True,
              )
              t.start()
  
          in_queues.append(in_queue)
          out_queues.append(out_queue)
  
      return (in_queues, out_queues)
  
file_path: pytorch/torch/distributed/pipeline/sync/worker.py
imports: from contextlib import contextmanager
  from queue import Queue
  import sys
  from threading import Thread
  from types import TracebackType
  from typing import TYPE_CHECKING, Callable, Dict, Generator, List, Optional, Tuple, Type, Union, cast
  import torch
  from .microbatch import Batch
  from .stream import AbstractStream, use_device, use_stream
label_desc: Use image node_id 2 for calling a global function with example usage: create_workers(devices) and returns: in_queues,  out_queues, device, torch, torch