code: def test_sequential(self):
      class ConvBNReLUModule(nn.Sequential):
          def __init__(self):
              super().__init__(
                  nn.Conv2d(3, 3, 1, 1, bias=False),
                  nn.BatchNorm2d(3),
                  nn.ReLU(inplace=False),
              )
  
      class ModelWithClassifierHead(nn.Module):
          def __init__(self):
              super().__init__()
              self.conv1 = nn.Conv2d(3, 3, 1)
              self.relu1 = nn.ReLU(inplace=False)
              layers = []
              for i in range(3):
                  layers.append(ConvBNReLUModule())
              self.features = nn.Sequential(*layers)
              head = [nn.Linear(300, 10), nn.ReLU(inplace=False)]
              self.classifier = nn.Sequential(*head)
              self.seq = nn.Sequential()
              self.quant = torch.ao.quantization.QuantStub()
              self.dequant = torch.ao.quantization.DeQuantStub()
  
          def forward(self, x):
              x = self.quant(x)
              x = self.conv1(x)
              x = self.relu1(x)
              x = self.features(x)
              x = torch.reshape(x, (-1, 3 * 10 * 10))
              x = self.classifier(x)
              x = self.seq(x)
              x = self.dequant(x)
              return x
  
      model = ModelWithClassifierHead().eval()
      torch.ao.quantization.fuse_modules(
          model,
          [
              ["conv1", "relu1"],
              ["features.0.0", "features.0.1", "features.0.2"],
              ["features.1.0", "features.1.1", "features.1.2"],
              ["features.2.0", "features.2.1", "features.2.2"],
          ],
          inplace=True,
      )
  
      x = np.random.rand(1, 3, 10, 10).astype("float32")
      self.generic_test(
          model, (x,), input_names=["x"], relaxed_check=True
      )
  
imports: import io
  import caffe2.python.onnx.backend
  import numpy
  import onnx
  import pytorch_test_common
  import torch.ao.nn.quantized
  import torch.nn
  import torch.onnx
  from torch.testing._internal import common_utils
label_desc: Use image node_id 16 for calling the TestQuantizedOps obj's underlying member method code with example usage: obj.test_sequential() and returns: x