code: def test_wildcard(self, dtype):
      class M(nn.Module):
          def __init__(self):
              super().__init__()
              self.conv1 = nn.Conv2d(32, 32, 3, padding=1, bias=True)
              self.eltwise = nn.ReLU()
  
          def forward(self, x):
              x = self.conv1(x)
              y = self.eltwise(x)
              return [x, y]
  
      # The pattern is as the following:
      #      conv
      #     |    \
      # eltwise   \
      #    |       \
      #  ListConstruct
      #
      # The output of conv is used by a wildcard op: ListConstruct.
      # Thus conv-eltwise cannot be selected into the same Partition.
      m = M()
      x = torch.rand(1, 32, 28, 28)
      _, graph = self.checkTrace(m, [x], dtype)
      # conv can exist in a single-op oneDNN Graph partition but not relu
      self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
      self.assertFused(graph, ["aten::_convolution"])
  
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 10 for calling the TestFusionPattern obj's underlying member method code with example usage: obj.test_wildcard(dtype) and returns: list