code: def generate_opcheck_tests(
      testcase: Any,
      namespaces: List[str],
      failures_dict_path: Optional[str] = None,
      additional_decorators: Dict[str, Callable] = None,
      test_utils: List[str] = DEFAULT_TEST_UTILS,
  ) -> None:
      """Given an existing TestCase, use the existing tests to generate
      additional validation tests for custom operators.
  
      For {all existing tests in the TestCase} x {all test utils},
      we will generate one new test. The new test runs a TorchFunctionMode
      that intercepts ``op(*args, **kwargs)`` calls and invokes
      ``test_util(op, *args, **kwargs)``, where ``op`` is an operator.
  
      The test_util that we support are in ALL_TEST_UTILS. They are:
      - test_schema: This runs SchemaCheckMode.
      - test_autograd_registration: This runs autograd_registration_check.
      - test_faketensor: This runs CrossRefFakeMode.
      - test_aot_dispatch_static: This runs aot_autograd_check, which:
          checks that the outputs (and gradients, if they are computable)
          are the same under eager-mode PyTorch and using AOTAutograd.
      - test_aot_dispatch_dynamic: Same as aot_dispatch_static, but
          runs AOTAutograd using dynamic shapes instead of static shapes.
  
      The generated test will have name ``{test_util}__{original_name}``.
      For example, if there is a method named ``test_cumsum``, then
      we will generate a ``test_schema__test_cumsum``,
      ``test_faketensor__test_cumsum``, etc.
  
      For more details, see https://docs.google.com/document/d/1Pj5HRZvdOq3xpFpbEjUZp2hBovhy7Wnxw14m6lF2154/edit
  
      Args:
          testcase: The testcase we will modify and generate additional tests for.
          namespaces: We will only intercept calls to custom operators with these
                      namespaces.
          failures_dict_path: See ``validate_failures_dict_structure`` for more details
          test_utils: a list of test_utils to generate. Example: ["test_schema", "test_faketensor"]
      """
      if additional_decorators is None:
          additional_decorators = {}
      test_methods = [
          m
          for m in dir(testcase)
          if m.startswith("test_") and callable(getattr(testcase, m))
      ]
      if failures_dict_path is None:
          # The default failures_dict_path is failures_dict.json in
          # the same directory as the test file.
          prev_frame = inspect.currentframe().f_back
          filename = inspect.getframeinfo(prev_frame)[0]
          failures_dict_path = get_file_path_2(
              os.path.dirname(filename), "failures_dict.json"
          )
      failures_dict = FailuresDict.load(
          failures_dict_path, create_file=should_update_failures_dict()
      )
      validate_failures_dict_structure(
          failures_dict, test_utils, testcase
      )
      validate_failures_dict_formatting(failures_dict_path)
  
      def construct_method(attr, prefix, tester):
          method = getattr(testcase, attr)
          if getattr(
              method, "_torch_dont_generate_opcheck_tests", False
          ):
              return
          new_method_name = prefix + "__" + attr
  
          @functools.wraps(method)
          def new_method(*args, **kwargs):
              with OpCheckMode(
                  namespaces,
                  prefix,
                  tester,
                  failures_dict,
                  f"{testcase.__name__}.{new_method_name}",
                  failures_dict_path,
              ):
                  result = method(*args, **kwargs)
              return result
  
          if pytestmark := new_method.__dict__.get("pytestmark"):
              import pytest
  
              # check if we need to simplify the parametrize marks
              # NB: you need to add this mark to your pytest.ini
              opcheck_only_one = False
              for mark in pytestmark:
                  if (
                      isinstance(mark, pytest.Mark)
                      and mark.name == "opcheck_only_one"
                  ):
                      opcheck_only_one = True
  
              if opcheck_only_one:
                  new_pytestmark = []
                  for mark in pytestmark:
                      if (
                          isinstance(mark, pytest.Mark)
                          and mark.name == "parametrize"
                      ):
                          argnames, argvalues = mark.args
                          assert not mark.kwargs, "NYI"
                          # Special case for device, we want to run on all
                          # devices
                          if argnames != "device":
                              new_pytestmark.append(
                                  pytest.mark.parametrize(
                                      argnames, (next(iter(argvalues)),)
                                  )
                              )
                              continue
                      new_pytestmark.append(mark)
                  new_method.__dict__["pytestmark"] = new_pytestmark
  
          if new_method_name in additional_decorators:
              for dec in additional_decorators[new_method_name]:
                  new_method = dec(new_method)
  
          if hasattr(testcase, new_method_name):
              raise RuntimeError(
                  f"Tried to autogenerate {new_method_name} but {testcase} already "
                  f"has method named {new_method_name}. Please rename the original "
                  f"method on the TestCase."
              )
          setattr(testcase, new_method_name, new_method)
  
      test_utils = {name: ALL_TEST_UTILS[name] for name in test_utils}
      for attr in test_methods:
          for prefix, tester in test_utils.items():
              construct_method(attr, prefix, tester)
  
      generate_tag_tests(testcase, failures_dict, additional_decorators)
  
imports: import datetime
  import difflib
  import functools
  import inspect
  import json
  import os
  import re
  import tempfile
  import threading
  import unittest
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  import torch._dynamo
  import torch.utils._pytree
  from torch._dynamo.utils import clone_input
  from torch._subclasses.schema_check_mode import SchemaCheckMode
  from torch._utils_internal import get_file_path_2
  from torch.overrides import TorchFunctionMode
  from torch.testing._internal.optests import aot_autograd_check, autograd_registration_check, fake_check
label_desc: Use image node_id 8 for calling a global function with example usage: generate_opcheck_tests(testcase, namespaces, failures_dict_path, additional_decorators, test_utils) and returns: result