code: def test_comprehensive(self, device, dtype, op):
      torch._dynamo.reset()
      with torch.no_grad():
          torch.cuda.empty_cache()
      op_name = op.name
      if op.variant_test_name:
          op_name += f".{op.variant_test_name}"
  
      # Skip dtype=torch.uint8 for all ops except upsample and interpolate:
      allowed_dtypes = [f16, f32, f64, i32, i64, b8]
      if op_name not in (
          "nn.functional.interpolate.bilinear",
          "nn.functional.upsample_bilinear",
          "nn.functional.upsample_nearest",
      ):
          if dtype not in allowed_dtypes:
              raise unittest.SkipTest("Skipped!")
  
      device_type = torch.device(device).type
  
      assert device_type in ("cuda", "cpu")
  
      # with open("test_output.txt", "a") as f:
      #     print(f"CONSIDERING OP {op_name} on {device_type} with {dtype} |
      # {inductor_skips[device_type].get(op_name, set())}", flush=True, file=f)
      #     print(f"CONSIDERING OP {op_name} on {device_type} with {dtype} |
      # {inductor_skips[device_type].get(op_name, set())}", flush=True)
      if dtype in inductor_skips[device_type].get(op_name, set()):
          test_expect = ExpectedTestResult.SKIP
          # with open("test_output.txt", "a") as f:
          #     print(f"SKIPPING OP {op_name} on {device_type}", flush=True, file=f)
          #     print(f"SKIPPING OP {op_name} on {device_type}", flush=True)
      elif dtype in inductor_expected_failures_single_sample[
          device_type
      ].get(
          op_name, set()
      ) or dtype in inductor_gradient_expected_failures_single_sample[
          device_type
      ].get(
          op_name, set()
      ):
          test_expect = ExpectedTestResult.XFAILURE
      else:
          test_expect = ExpectedTestResult.SUCCESS
  
      overridden_kwargs = {}
      if op_name in inductor_override_kwargs:
          overridden_kwargs = inductor_override_kwargs[op_name]
      elif (op_name, device_type) in inductor_override_kwargs:
          overridden_kwargs = inductor_override_kwargs[
              (op_name, device_type)
          ]
      elif (op_name, device_type, dtype) in inductor_override_kwargs:
          overridden_kwargs = inductor_override_kwargs[
              (op_name, device_type, dtype)
          ]
  
      func = op.get_op()
  
      def fn(*args, **kwargs):
          return func(*args, **kwargs)
  
      requires_grad = (
          op.supports_autograd
          and dtype in op.supported_backward_dtypes(device_type)
          # TODO: OpInfo really ought to error out for this case, but it's
          # not exercised in test_ops_gradients atm.  The problem is not
          # complex32 per-se (which is supported by data movement only ops)
          # but that when we do backwards we expect other ops like add to work
          and not dtype == torch.complex32
      )
      samples = op.sample_inputs(
          device, dtype, requires_grad=requires_grad
      )
  
      if op_name not in inductor_all_samples and not ALL_SAMPLES:
          if isinstance(samples, (list, tuple)):
              samples = [samples[0]]
          else:
              samples = [next(samples)]
  
      class HasRngOp(TorchDispatchMode):
          def __init__(self):
              super().__init__()
              self.has_rng_op = False
  
          def __torch_dispatch__(self, func, types, args, kwargs=None):
              kwargs = kwargs if kwargs else {}
              if torch.Tag.nondeterministic_seeded in func.tags:
                  self.has_rng_op = True
  
              return func(*args, **kwargs)
  
      def do_nopython_and_has_rng(fn, args, kwargs):
          try:
              mode = FakeTensorMode()
  
              def map_to_fake(e):
                  if isinstance(e, torch.Tensor):
                      return mode.from_tensor(e)
                  else:
                      return e
  
              args, kwargs = tree_map(map_to_fake, (args, kwargs))
              with HasRngOp() as rng_mode, mode:
                  with enable_python_dispatcher():
                      fn(*args, **kwargs)
  
          except (
              DataDependentOutputException,
              DynamicOutputShapeException,
          ):
              return False, rng_mode.has_rng_op
  
          return True, rng_mode.has_rng_op
  
      def get_contexts(has_rng_op):
          if has_rng_op:
              # TODO - enable this, running into errors
              return (
                  # (
                  #     lambda: torch._inductor.config.patch(
                  #         {"fallback_random": True, "implicit_fallbacks": True}
                  #     ),
                  #     {"assert_equal": True},
                  # ),
                  (
                      contextlib.nullcontext,
                      {"assert_equal": False},
                  ),
              )
          return ((contextlib.nullcontext, {}),)
  
      try:
  
          def _get_tolerances(dtype):
              _custom_tolerances = {
                  torch.float32: (1.3e-5, 1.5e-5),
              }
              if dtype in _custom_tolerances:
                  return _custom_tolerances[dtype]
              else:
                  return None, None
  
          for sample_input in samples:
              args = [sample_input.input] + list(sample_input.args)
              kwargs = sample_input.kwargs
              # UNCOMMENT TO DEBUG SEGFAULTS
  
              # with open("test_output.txt", "a") as f:
              #     print(f"RUNNING OP {op_name} on {device_type} with {dtype}", flush=True, file=f)
              #     print(f"RUNNING OP {op_name} on {device_type} with {dtype}", flush=True)
              rtol, atol = _get_tolerances(dtype)
              if device_type == "cuda":
                  # opinfo test case have already place the input on the correct device
                  # so we don't need do additional copy by setting copy_to_cuda=False
  
                  no_python, has_rng_op = do_nopython_and_has_rng(
                      fn, args, kwargs
                  )
                  for context_fn, kwarg_overrides in get_contexts(
                      has_rng_op
                  ):
                      with context_fn():
                          adjusted_kwargs = {
                              "check_lowp": False,
                              "nopython": no_python,
                              "copy_to_cuda": False,
                              "reference_in_float": False,
                              "check_gradient": requires_grad,
                              "check_has_compiled": no_python,
                              "output_process_fn_grad": sample_input.output_process_fn_grad,
                              "atol": atol,
                              "rtol": rtol,
                          }
                          adjusted_kwargs.update(overridden_kwargs)
                          adjusted_kwargs.update(kwarg_overrides)
                          self.check_model_cuda(
                              fn,
                              args,
                              kwargs,
                              **adjusted_kwargs,
                          )
              elif device_type == "cpu":
                  no_python, has_rng_op = do_nopython_and_has_rng(
                      fn, args, kwargs
                  )
                  for context_fn, kwarg_overrides in get_contexts(
                      has_rng_op
                  ):
                      with context_fn():
                          adjusted_kwargs = {
                              "check_lowp": False,
                              "nopython": no_python,
                              "check_has_compiled": no_python,
                              # skip checking gradient on CPU for now
                              "check_gradient": False,
                              "atol": atol,
                              "rtol": rtol,
                          }
                          adjusted_kwargs.update(overridden_kwargs)
                          adjusted_kwargs.update(kwarg_overrides)
  
                          self.check_model(
                              fn,
                              args,
                              kwargs,
                              **adjusted_kwargs,
                          )
  
      except Exception as e:
          known_failure = False
          if dtype in inductor_should_fail_with_exception[
              device_type
          ].get(op_name, set()):
              failure = inductor_should_fail_with_exception[
                  device_type
              ][op_name][dtype]
              if failure in str(e):
                  known_failure = True
          if not known_failure:
              raise e
  
imports: import atexit
  import contextlib
  import functools
  import os
  import sys
  import unittest
  from collections import defaultdict
  from enum import Enum
  from functools import partial
  from unittest.mock import patch
  import torch
  from torch._dispatch.python import enable_python_dispatcher
  from torch._dynamo.test_case import run_tests
  from torch._subclasses.fake_tensor import DataDependentOutputException, DynamicOutputShapeException, FakeTensorMode
  from torch.testing._internal.common_cuda import SM80OrLater
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyNativeDeviceTypes, OpDTypes, ops, skipCPUIf, skipCUDAIf
  from torch.testing._internal.common_methods_invocations import op_db, skipOps
  from torch.testing._internal.common_utils import dtype_abbrs, IS_MACOS, IS_X86, skipCUDAMemoryLeakCheckIf, skipIfCrossRef, skipIfTorchDynamo, suppress_warnings, TEST_MKL, TEST_WITH_ASAN, TEST_WITH_ROCM, TestCase
  from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils._pytree import tree_map
label_desc: Use image node_id 2 for calling the TestInductorOpInfo obj's underlying member method code with example usage: obj.test_comprehensive(device, dtype, op) and returns: func, True,  rng_mode, contextlib,  dict, func, contextlib,  dict, False,  rng_mode, _custom_tolerances, None,  None, mode, e