code: def __init__(
      self,
      kernel_name: str,
      input_tensor_meta: Union[TensorMeta, List[TensorMeta]],
      output_tensor_meta: Union[TensorMeta, List[TensorMeta]],
      extra_args: Iterable[Any],
      module_path: str,  # the path of the module defining the triton kernel
      module_cache_key: str,
      grid: List[int],
      num_stages: int,
      num_warps: int,
  ):
      super().__init__(
          kernel_name, input_tensor_meta, output_tensor_meta, extra_args
      )
      self.module_path = module_path
      self.module_cache_key = module_cache_key
      self.grid = grid
      self.num_stages = num_stages
      self.num_warps = 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 1 to create a new TritonBenchmarkRequest object from inherited base classes: BenchmarkRequest with example: obj = TritonBenchmarkRequest(kernel_name, input_tensor_meta, output_tensor_meta, extra_args, module_path, module_cache_key, grid, num_stages, num_warps)