code: def make_run_fn(
      self, *input_tensors: torch.Tensor, output_tensor: torch.Tensor
  ) -> Callable[[], None]:
      mod = PyCodeCache.load_by_key_path(
          self.module_cache_key, self.module_path
      )
      log.debug(
          "benchmark module key: %s, path: %s",
          self.module_cache_key,
          self.module_path,
      )
  
      run_method = getattr(mod, self.kernel_name).run
      extra_args = list(self.extra_args)
  
      # Newer version of triton add warmup argument to JITFunction.run.
      # This code handles backward-compatibility.
      warmup_arg = {}
      import inspect
  
      if "warmup" in inspect.signature(run_method).parameters:
          warmup_arg["warmup"] = False
  
      return functools.partial(
          run_method,
          *input_tensors,
          output_tensor,
          *self.extra_args,
          grid=self.grid,
          **warmup_arg,
          num_stages=self.num_stages,
          num_warps=self.num_warps,
      )
  
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 2 for calling the TritonBenchmarkRequest obj's underlying member method code with example usage: obj.make_run_fn() and returns: functools