code: def test_tuning_pool_crash(self):
      # Use only one device/subprocess so we test the process restarts
      # and is usable after a "crash".
      with config.patch({"autotune_multi_device": False}):
          tuning_pool = TuningProcessPool()
          tuning_pool.initialize()
  
          # First force the tuning process to "crash" by setting a bogus
          # string for the expected visible devices.
          bmreq = TestBenchmarkRequest(3.14, False, "invalid")
          choice = TestTritonTemplateCaller(bmreq)
  
          timings = tuning_pool.benchmark([choice])
          self.assertTrue(choice in timings)
          self.assertEqual(timings[choice], float("inf"))
  
          # Then send another request and make sure the sub-process
          # has restarted and is operational. 'valid_devices' expected
          # to be None because autotune_multi_device is off.
          choice.bmreq.parent_visible_devices = os.environ.get(
              CUDA_VISIBLE_DEVICES
          )
  
          timings = tuning_pool.benchmark([choice])
          self.assertTrue(choice in timings)
          self.assertEqual(timings[choice], bmreq.value)
  
          tuning_pool.terminate()
  
imports: import os
  import unittest
  from typing import Callable, List, Optional
  import torch
  from torch import multiprocessing
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.testing import reset_rng_state
  from torch._dynamo.utils import counters
  from torch._inductor import config
  from torch._inductor.autotune_process import BenchmarkRequest, CUDA_VISIBLE_DEVICES, TuningProcessPool
  from torch._inductor.graph import GraphLowering
  from torch._inductor.ir import Buffer, FixedLayout
  from torch._inductor.kernel.mm_plus_mm import aten_mm_plus_mm
  from torch._inductor.select_algorithm import AlgorithmSelectorCache, ChoiceCaller, TritonTemplateCaller
  from torch._inductor.utils import run_and_get_code
  from torch._inductor.virtualized import V
  from torch.fx.experimental.proxy_tensor import make_fx
  from torch.testing import FileCheck
  from torch.testing._internal.common_cuda import SM75OrLater, SM90OrLater
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, skipIfRocm
  from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
label_desc: Use image node_id 1 for calling the TestTuningProcess obj's underlying member method code with example usage: obj.test_tuning_pool_crash() without return types