code: def test_conv2d_bn_relu(self, dtype):
      class M(nn.Module):
          def __init__(self):
              super().__init__()
              self.conv1 = nn.Conv2d(32, 32, 3, padding=1, bias=True)
              self.bn1 = nn.BatchNorm2d(32)
  
          def forward(self, x):
              x = self.conv1(x)
              x = self.bn1(x)
              x = F.relu(x)
              return x
  
      m = M().eval()
      if dtype == torch.bfloat16:
          m = optimization.fuse(m)
      x = torch.rand(1, 32, 28, 28)
      _, graph = self.checkTrace(m, [x], dtype)
      self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
      self.assertFused(
          graph,
          ["aten::_convolution", "aten::batch_norm", "aten::relu"],
      )
  
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 6 for calling the TestFusionPattern obj's underlying member method code with example usage: obj.test_conv2d_bn_relu(dtype) and returns: x