code: def op_assert_ref(
      test_case, op, test_dtype, i, orig, decomp, ref, args, kwargs
  ):
      assert orig.dtype == decomp.dtype, f"{i} Operation:  {op}"
      if orig.numel() == 0 or decomp.numel() == 0:
          assert orig.numel() == decomp.numel()
          return
      assert orig.shape == decomp.shape, f"{i} Operation:  {op}"
      tol_table = {
          (
              torch.bfloat16,
              torch.ops.aten.native_layer_norm.default,
          ): 1e-5,
          (
              torch.float16,
              torch.ops.aten.native_layer_norm.default,
          ): 1e-5,
          (
              torch.float16,
              torch.ops.aten.native_layer_norm_backward.default,
          ): 1e-3,
          (
              torch.bfloat16,
              torch.ops.aten.native_layer_norm_backward.default,
          ): 2e-2,
          (
              torch.bfloat16,
              torch.ops.aten.native_batch_norm.default,
          ): 1e-5,
          (
              torch.float16,
              torch.ops.aten.native_batch_norm.default,
          ): 1e-5,
          (
              torch.bfloat16,
              torch.ops.aten._native_batch_norm_legit.default,
          ): 1e-5,
          (
              torch.bfloat16,
              torch.ops.aten._native_batch_norm_legit.no_stats,
          ): 1e-5,
          (
              torch.float16,
              torch.ops.aten._native_batch_norm_legit.default,
          ): 1e-5,
          (
              torch.float16,
              torch.ops.aten._native_batch_norm_legit.no_stats,
          ): 1e-5,
          (
              torch.bfloat16,
              torch.ops.aten.linalg_vector_norm.default,
          ): 1e-4,
          (
              torch.float16,
              torch.ops.aten.linalg_vector_norm.default,
          ): 1e-4,
          (torch.bfloat16, torch.ops.aten.var_mean.correction): 5e-7,
          (torch.float16, torch.ops.aten.var_mean.correction): 5e-7,
          (torch.bfloat16, torch.ops.aten.var_mean.dim): 5e-7,
          (torch.float16, torch.ops.aten.var_mean.dim): 5e-7,
          (
              torch.float16,
              torch.ops.aten.nll_loss_forward.default,
          ): 1e-2,
          (
              torch.bfloat16,
              torch.ops.aten.nll_loss_forward.default,
          ): 1e-1,
          (
              torch.float16,
              torch.ops.aten.nll_loss2d_forward.default,
          ): 1e-2,
          (
              torch.bfloat16,
              torch.ops.aten.nll_loss2d_forward.default,
          ): 2e-1,
          # see https://github.com/pytorch/pytorch/pull/96264
          (torch.float16, torch.ops.aten.mv.default): 1e-5,
      }
      if ref.is_floating_point():
          orig_diff = (orig - ref).abs().max()
          decomp_diff = (decomp - ref).abs().max()
          atol = tol_table.get((test_dtype, op), 1e-7)
          if decomp_diff > orig_diff + atol:
              raise RuntimeError(
                  f"Difference from float64 is larger with decomposition {op.__name__}"
                  f" than original on output {i}. Original max diff: {orig_diff}, Decomp max diff: {decomp_diff}\n"
                  f"atol = {atol}\n"
                  f"args = {args}\n"
                  f"kwargs = {kwargs}"
              )
      else:
          test_case.assertEqual(
              orig,
              decomp,
              msg=f"{op.__name__}\nargs = {args}\nkwargs = {kwargs}",
          )
  
imports: from collections import defaultdict
  from torch import Tensor
  import torch.autograd
  from torch._decomp import core_aten_decompositions, decomposition_table
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten
  from torch.utils import _pytree
  from torch.testing import make_tensor
  from torch.testing._internal.common_cuda import tf32_off
  from torch.testing._internal.common_utils import unMarkDynamoStrictTest
  from torch.testing._internal.common_utils import is_iterable_of_tensors, IS_WINDOWS, TestCase, skipIfCrossRef, suppress_warnings, TEST_WITH_ASAN, TEST_WITH_SLOW, run_tests, skipIfTorchDynamo
  from torch.testing._internal.common_modules import module_db, modules
  from torch.testing._internal.common_device_type import onlyNativeDeviceTypes, ops, instantiate_device_type_tests, onlyCPU, onlyCUDA
  from torch.testing._internal.common_methods_invocations import op_db, skip, skipOps, xfail
  from torch._dispatch.python import enable_python_dispatcher
  from torch._ops import DispatchKey
  import itertools
  import functools
  from functools import partial
  import unittest
label_desc: Use image node_id 7 for calling a global function with example usage: op_assert_ref(test_case, op, test_dtype, i, orig, decomp, ref, args, kwargs) without return types