code: def _test_tautological_mm(
      self,
      device: str = "cuda",
      x_dtype: torch.dtype = torch.float8_e4m3fn,
      y_dtype: torch.dtype = torch.float8_e4m3fn,
      out_dtype: Optional[torch.dtype] = None,
      size: int = 16,
  ) -> None:
      x_fp8 = torch.rand(size, size, device=device).to(x_dtype)
      y_fp8 = torch.eye(size, device=device, dtype=y_dtype).t()
      out_fp32 = torch.mm(x_fp8.to(torch.float), y_fp8.to(torch.float))
      (out_fp8, amax_fp8) = torch._scaled_mm(
          x_fp8, y_fp8, out_dtype=out_dtype
      )
      if out_dtype is not None:
          self.assertEqual(out_dtype, out_fp8.dtype)
      if out_dtype not in [torch.float16, torch.bfloat16, torch.float]:
          self.assertEqual(out_fp32.amax(), amax_fp8)
      self.assertEqual(out_fp32, out_fp8.to(torch.float))
  
imports: import unittest
  from itertools import product
  from functools import partial
  from typing import Optional
  import torch
  from torch.quantization._quantized_conversions import pack_int4_to_int8, quantized_weight_reorder_for_mixed_dtypes_linear_cutlass
  from torch.testing import make_tensor
  from torch.testing._internal.common_cuda import SM53OrLater, _get_torch_cuda_version
  from torch.testing._internal.common_device_type import dtypes, instantiate_device_type_tests, onlyCUDA, tol, toleranceOverride
  from torch.testing._internal.common_utils import IS_ARM64, IS_JETSON, IS_WINDOWS, parametrize, run_tests, skipIfRocmVersionLessThan, TEST_WITH_ROCM, skipIfRocm, TestCase
label_desc: Use image node_id 1 for calling the TestFP8MatmulCuda obj's underlying member method code with example usage: obj._test_tautological_mm(device, x_dtype, y_dtype, out_dtype, size) without return types