code: def test_small_model(self):
      class SimpleModel(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.quant = torch.ao.quantization.QuantStub()
              self.dequant = torch.ao.quantization.DeQuantStub()
              self.func_add = nnq.FloatFunctional()
              self.conv1 = nn.Conv2d(3, 2, 5, bias=None).to(
                  dtype=torch.float
              )
              self.act1 = nn.Sigmoid()
              self.conv2 = nn.Conv2d(2, 2, 1, bias=None).to(
                  dtype=torch.float
              )
              self.fc = nn.Linear(72, 10).to(dtype=torch.float)
              self.fc.qconfig = None
  
          def forward(self, x):
              x = self.quant(x)
              x = self.func_add.add(x, x)
              x = self.conv1(x)
              x = self.act1(x)
              x = self.conv2(x)
              x = self.dequant(x)
              x = x.reshape(-1, 72).contiguous()
              x = self.fc(x)
              return x
  
      x = np.random.rand(2, 3, 10, 10).astype("float32")
      self.generic_test(
          SimpleModel(), (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 15 for calling the TestQuantizedOps obj's underlying member method code with example usage: obj.test_small_model() and returns: x