code: def skipOps(test_case_name, base_test_name, to_skip):
      all_opinfos = op_db + additional_op_db + autograd_function_db
      for decorate_meta in to_skip:
          matching_opinfos = [
              o
              for o in all_opinfos
              if o.name == decorate_meta.op_name
              and o.variant_test_name == decorate_meta.variant_name
          ]
          assert (
              len(matching_opinfos) > 0
          ), f"Couldn't find OpInfo for {decorate_meta}"
          assert len(matching_opinfos) == 1, (
              "OpInfos should be uniquely determined by their (name, variant_name). "
              f"Got more than one result for ({decorate_meta.op_name}, {decorate_meta.variant_name})"
          )
          opinfo = matching_opinfos[0]
          decorators = list(opinfo.decorators)
          new_decorator = DecorateInfo(
              decorate_meta.decorator,
              test_case_name,
              base_test_name,
              device_type=decorate_meta.device_type,
              dtypes=decorate_meta.dtypes,
          )
          decorators.append(new_decorator)
          opinfo.decorators = tuple(decorators)
  
      # This decorator doesn't modify fn in any way
      def wrapped(fn):
          return fn
  
      return wrapped
  
imports: import itertools
  import torch
  from functorch import vmap
  import torch.utils._pytree
  from functorch_additional_op_db import additional_op_db
  from torch.testing._internal.common_methods_invocations import DecorateInfo
  from torch.testing._internal.common_methods_invocations import op_db
  from torch.testing._internal.common_modules import module_db
  import os
  import unittest
  from torch.testing._internal.common_device_type import toleranceOverride
  from torch.testing._internal.autograd_function_db import autograd_function_db
  from collections import namedtuple
label_desc: Use image node_id 19 for calling a global function with example usage: skipOps(test_case_name, base_test_name, to_skip) and returns: wrapped, fn