code: def create_pool(
      num_workers: int,
      subprocess_name: Optional[str] = None,
      subprocess_init: Optional[Callable[[], None]] = None,
  ) -> mp.Pool:
      # PyTorch relies on OpenMP, which by default parallelizes operations by
      # implicitly spawning as many threads as there are cores, and synchronizing
      # them with each other. This interacts poorly with Hogwild!-style subprocess
      # pools as if each child process spawns its own OpenMP threads there can
      # easily be thousands of threads that mostly wait in barriers. Calling
      # set_num_threads(1) in both the parent and children prevents this.
      # OpenMP can also lead to deadlocks if it gets initialized in the parent
      # process before the fork (this bit us in unit tests, due to the generation
      # of the test input data). Using the "spawn" context (i.e., fork + exec)
      # solved the issue in most cases but still left some deadlocks. See
      # https://github.com/pytorch/pytorch/issues/17199 for some more information
      # and discussion.
      torch.set_num_threads(1)
      return mp.get_context("spawn").Pool(
          num_workers,
          initializer=_pool_init,
          initargs=(subprocess_name, subprocess_init),
      )
  
imports: import logging
  import multiprocessing
  import multiprocessing.pool
  import os
  import os.path
  import sys
  from collections import defaultdict
  from functools import partial
  from typing import Any, Callable, Dict, Iterable, List, Mapping, MutableMapping, Optional, Set, Tuple, TypeVar, Union
  import torch
  import torch.multiprocessing
  from torch.optim import Optimizer
  from torchbiggraph.config import ConfigSchema
  from torchbiggraph.types import Bucket, EntityName, FloatTensorType, Partition, Side
label_desc: Use image node_id 13 for calling a global function with example usage: create_pool(num_workers, subprocess_name, subprocess_init) and returns: mp