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],
      source_code: str,
  ):
      super().__init__(
          kernel_name, input_tensor_meta, output_tensor_meta, extra_args
      )
      self.source_code = source_code
      self.workspace_size: int = 0
      self.workspace: Optional[torch.Tensor] = None
      self.DLL: Optional[DLLWrapper] = None
      self.hash_key: str = ""
      self.source_file: str = ""
      self.hash_key, self.source_file = CUDACodeCache.write(
          self.source_code, "so"
      )
  
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 CUDABenchmarkRequest object from inherited base classes: BenchmarkRequest with example: obj = CUDABenchmarkRequest(kernel_name, input_tensor_meta, output_tensor_meta, extra_args, source_code)