code: def test_dynamo_aot_ts_onednn(self):
      class Seq(nn.Module):
          def __init__(self):
              super().__init__()
              self.layers = nn.Sequential(
                  nn.Linear(10, 10),
                  nn.ReLU(),
                  nn.Linear(10, 10),
                  nn.ReLU(),
              )
  
          def forward(self, x):
              return self.layers(x)
  
      mod = Seq()
  
      import torch._dynamo
  
      aot_mod = torch._dynamo.optimize("aot_ts", nopython=True)(mod)
  
      for _ in range(10):
          with torch.jit.fuser("fuser3"):
              loss = aot_mod(torch.rand([10, 10])).sum()
              loss.backward()
  
      torch._dynamo.reset()
  
imports: import sys
  import torch
  import unittest
  import itertools
  import torch.nn
  from functools import wraps
  from concurrent import futures
  import torch.nn.functional
  import torch.fx.experimental.optimization
  from torch.testing._internal.jit_utils import JitTestCase
  from torch.testing._internal.common_utils import run_tests, TEST_SCIPY, IS_WINDOWS, IS_MACOS
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyCPU, dtypes
label_desc: Use image node_id 1 for calling the TestDynamoAOT obj's underlying member method code with example usage: obj.test_dynamo_aot_ts_onednn() and returns: self