code: def test_sdpa(self, device):
      from torch.fx.experimental.proxy_tensor import make_fx
      from torch._decomp import get_decompositions
      from torch.nn import functional as F
  
      class ScaledDotProductAttention(torch.nn.Module):
          def __init__(self):
              super().__init__()
  
          def forward(self, query_layer, key_layer, value_layer):
              attn_output = F.scaled_dot_product_attention(
                  query_layer,
                  key_layer,
                  value_layer,
                  None,
                  dropout_p=0.0,
                  is_causal=True,
              )
              return attn_output
  
      query_layer = torch.randn(1, 128, 100, 64, device=device)
      key_layer = torch.randn(1, 128, 100, 64, device=device)
      value_layer = torch.randn(1, 128, 100, 64, device=device)
  
      attention = ScaledDotProductAttention()
      fx_g = make_fx(
          attention,
          decomposition_table=get_decompositions(
              [
                  torch.ops.aten._scaled_dot_product_flash_attention_for_cpu.default,
              ]
          ),
      )(query_layer, key_layer, value_layer)
  
      compiled_res = fx_g(query_layer, key_layer, value_layer)
      eager_res = F.scaled_dot_product_attention(
          query_layer,
          key_layer,
          value_layer,
          None,
          dropout_p=0.0,
          is_causal=True,
      )
      self.assertTrue(
          torch.allclose(compiled_res, eager_res, atol=1e-6, rtol=1e-5)
      )
  
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 the DecompOneOffTests obj's underlying member method code with example usage: obj.test_sdpa(device) and returns: attn_output