class_docstr: Abstraction for launching a helper process to benchmark kernels. Spawns
  the parent process and uses multiprocessing queues to send benchmark
  requests and return results.
class_docstr_tok: ["Abstraction","for","launching","a","helper","process","to","benchmark","kernels",".","Spawns","the","parent","process","and","uses","multiprocessing","queues","to","send","benchmark","requests","and","return","results","."]
docstr: Signal the child process to terminate.
docstr_tok: ["Signal","the","child","process","to","terminate","."]
code: def terminate(self) -> None:
      """
      Signal the child process to terminate.
      """
      if self.valid():
          assert self.process is not None
          assert self.request_queue is not None
          self.request_queue.put(None)
  
imports: from __future__ import annotations
  import contextlib
  import dataclasses
  import functools
  import logging
  import os
  import queue
  import time
  import warnings
  from concurrent.futures import ThreadPoolExecutor
  from ctypes import byref, c_size_t, c_void_p
  from multiprocessing.process import BaseProcess
  from multiprocessing.queues import Queue
  from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, TYPE_CHECKING, Union
  import torch
  from torch import multiprocessing
  from torch._dynamo.testing import rand_strided
  from torch._inductor import ir
  from torch._inductor.codecache import CUDACodeCache, DLLWrapper, PyCodeCache
  from .None import config
  from .utils import do_bench
  from .virtualized import V
label_desc: Use image node_id 8 for calling the TuningProcess obj's underlying member method code with example usage: obj.terminate() without return types