My original model looks like:
ResNetCifar10(
  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU()
  (layer1): Sequential(
    (0): BasicBlockCifar10(
      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu1): ReLU()
      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu2): ReLU()
      (shortcut): Sequential()
    )
    (1): BasicBlockCifar10(
      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu1): ReLU()
      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu2): ReLU()
      (shortcut): Sequential()
    )
    (2): BasicBlockCifar10(
      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu1): ReLU()
      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu2): ReLU()
      (shortcut): Sequential()
    )
  )
  (layer2): Sequential(
    (0): BasicBlockCifar10(
      (conv1): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu1): ReLU()
      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu2): ReLU()
      (shortcut): LambdaLayer()
    )
    (1): BasicBlockCifar10(
      (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu1): ReLU()
      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu2): ReLU()
      (shortcut): Sequential()
    )
    (2): BasicBlockCifar10(
      (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu1): ReLU()
      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu2): ReLU()
      (shortcut): Sequential()
    )
  )
  (layer3): Sequential(
    (0): BasicBlockCifar10(
      (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu1): ReLU()
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu2): ReLU()
      (shortcut): LambdaLayer()
    )
    (1): BasicBlockCifar10(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu1): ReLU()
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu2): ReLU()
      (shortcut): Sequential()
    )
    (2): BasicBlockCifar10(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu1): ReLU()
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu2): ReLU()
      (shortcut): Sequential()
    )
  )
  (linear): Linear(in_features=64, out_features=10, bias=True)
)

I then convert it to a model that can be trained using QAT:
(quant and dequant, fuse layers and torch.quantization.prepare_qat)
QuantWrapper(
  (quant): QuantStub(
    (observer): FakeQuantize(
      fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
      (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
    )
  )
  (dequant): DeQuantStub(
    (observer): FakeQuantize(
      fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
      (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
    )
  )
  (module): ResNetCifar10(
    (conv1): ConvBnReLU2d(
      3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
      (observer): FakeQuantize(
        fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
        (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
      )
      (weight_fake_quant): FakeQuantize(
        fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
        (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
      )
    )
    (bn1): Identity()
    (relu): Identity()
    (layer1): Sequential(
      (0): BasicBlockCifar10(
        (conv1): ConvBnReLU2d(
          16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn1): Identity()
        (relu1): Identity()
        (conv2): ConvBn2d(
          16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn2): Identity()
        (relu2): ReLU(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
      )
      (1): BasicBlockCifar10(
        (conv1): ConvBnReLU2d(
          16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn1): Identity()
        (relu1): Identity()
        (conv2): ConvBn2d(
          16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn2): Identity()
        (relu2): ReLU(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
      )
      (2): BasicBlockCifar10(
        (conv1): ConvBnReLU2d(
          16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn1): Identity()
        (relu1): Identity()
        (conv2): ConvBn2d(
          16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn2): Identity()
        (relu2): ReLU(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
      )
    )
    (layer2): Sequential(
      (0): BasicBlockCifar10(
        (conv1): ConvBnReLU2d(
          16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn1): Identity()
        (relu1): Identity()
        (conv2): ConvBn2d(
          32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn2): Identity()
        (relu2): ReLU(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (shortcut): LambdaLayer()
      )
      (1): BasicBlockCifar10(
        (conv1): ConvBnReLU2d(
          32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn1): Identity()
        (relu1): Identity()
        (conv2): ConvBn2d(
          32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn2): Identity()
        (relu2): ReLU(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
      )
      (2): BasicBlockCifar10(
        (conv1): ConvBnReLU2d(
          32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn1): Identity()
        (relu1): Identity()
        (conv2): ConvBn2d(
          32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn2): Identity()
        (relu2): ReLU(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
      )
    )
    (layer3): Sequential(
      (0): BasicBlockCifar10(
        (conv1): ConvBnReLU2d(
          32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn1): Identity()
        (relu1): Identity()
        (conv2): ConvBn2d(
          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn2): Identity()
        (relu2): ReLU(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (shortcut): LambdaLayer()
      )
      (1): BasicBlockCifar10(
        (conv1): ConvBnReLU2d(
          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn1): Identity()
        (relu1): Identity()
        (conv2): ConvBn2d(
          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn2): Identity()
        (relu2): ReLU(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
      )
      (2): BasicBlockCifar10(
        (conv1): ConvBnReLU2d(
          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn1): Identity()
        (relu1): Identity()
        (conv2): ConvBn2d(
          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
          (weight_fake_quant): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (bn2): Identity()
        (relu2): ReLU(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
            (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
          )
        )
      )
    )
    (linear): Linear(
      in_features=64, out_features=10, bias=True
      (observer): FakeQuantize(
        fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
        (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
      )
      (weight_fake_quant): FakeQuantize(
        fake_quant_enabled=True, observer_enabled=True,            scale=None, zero_point=None
        (observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
      )
    )
  )
)

Which can be trained, but I then try to convert it to a evaluation model using
torch.quantization.convert like in the tutorial:
The converted model looks like:
QuantWrapper(
  (quant): Quantize(scale=tensor([0.0203]), zero_point=tensor([120]), dtype=torch.quint8)
  (dequant): DeQuantize()
  (module): ResNetCifar10(
    (conv1): QuantizedConvReLU2d(3, 16, kernel_size=(3, 3), stride=(1, 1), scale=0.00903782807290554, zero_point=0, padding=(1, 1))
    (bn1): Identity()
    (relu): Identity()
    (layer1): Sequential(
      (0): BasicBlockCifar10(
        (conv1): QuantizedConvReLU2d(16, 16, kernel_size=(3, 3), stride=(1, 1), scale=0.009634326212108135, zero_point=0, padding=(1, 1))
        (bn1): Identity()
        (relu1): Identity()
        (conv2): QuantizedConv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), scale=0.018731188029050827, zero_point=130, padding=(1, 1))
        (bn2): Identity()
        (relu2): QuantizedReLU()
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=tensor([0.0086]), zero_point=tensor([0])
            (observer): MovingAverageMinMaxObserver(min_val=0.0, max_val=2.1906778812408447)
          )
        )
      )
      (1): BasicBlockCifar10(
        (conv1): QuantizedConvReLU2d(16, 16, kernel_size=(3, 3), stride=(1, 1), scale=0.010925675742328167, zero_point=0, padding=(1, 1))
        (bn1): Identity()
        (relu1): Identity()
        (conv2): QuantizedConv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), scale=0.01969429850578308, zero_point=141, padding=(1, 1))
        (bn2): Identity()
        (relu2): QuantizedReLU()
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=tensor([0.0112]), zero_point=tensor([0])
            (observer): MovingAverageMinMaxObserver(min_val=0.0, max_val=2.8511974811553955)
          )
        )
      )
      (2): BasicBlockCifar10(
        (conv1): QuantizedConvReLU2d(16, 16, kernel_size=(3, 3), stride=(1, 1), scale=0.007963746786117554, zero_point=0, padding=(1, 1))
        (bn1): Identity()
        (relu1): Identity()
        (conv2): QuantizedConv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), scale=0.016999518498778343, zero_point=136, padding=(1, 1))
        (bn2): Identity()
        (relu2): QuantizedReLU()
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=tensor([0.0144]), zero_point=tensor([0])
            (observer): MovingAverageMinMaxObserver(min_val=0.0, max_val=3.661449670791626)
          )
        )
      )
    )
    (layer2): Sequential(
      (0): BasicBlockCifar10(
        (conv1): QuantizedConvReLU2d(16, 32, kernel_size=(3, 3), stride=(2, 2), scale=0.009311596862971783, zero_point=0, padding=(1, 1))
        (bn1): Identity()
        (relu1): Identity()
        (conv2): QuantizedConv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), scale=0.02033022604882717, zero_point=115, padding=(1, 1))
        (bn2): Identity()
        (relu2): QuantizedReLU()
        (shortcut): LambdaLayer()
      )
      (1): BasicBlockCifar10(
        (conv1): QuantizedConvReLU2d(32, 32, kernel_size=(3, 3), stride=(1, 1), scale=0.006638957187533379, zero_point=0, padding=(1, 1))
        (bn1): Identity()
        (relu1): Identity()
        (conv2): QuantizedConv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), scale=0.018797850236296654, zero_point=136, padding=(1, 1))
        (bn2): Identity()
        (relu2): QuantizedReLU()
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=tensor([0.0147]), zero_point=tensor([0])
            (observer): MovingAverageMinMaxObserver(min_val=0.0, max_val=3.7611701488494873)
          )
        )
      )
      (2): BasicBlockCifar10(
        (conv1): QuantizedConvReLU2d(32, 32, kernel_size=(3, 3), stride=(1, 1), scale=0.007839899510145187, zero_point=0, padding=(1, 1))
        (bn1): Identity()
        (relu1): Identity()
        (conv2): QuantizedConv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), scale=0.026490747928619385, zero_point=85, padding=(1, 1))
        (bn2): Identity()
        (relu2): QuantizedReLU()
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=tensor([0.0144]), zero_point=tensor([0])
            (observer): MovingAverageMinMaxObserver(min_val=0.0, max_val=3.678955316543579)
          )
        )
      )
    )
    (layer3): Sequential(
      (0): BasicBlockCifar10(
        (conv1): QuantizedConvReLU2d(32, 64, kernel_size=(3, 3), stride=(2, 2), scale=0.009626154787838459, zero_point=0, padding=(1, 1))
        (bn1): Identity()
        (relu1): Identity()
        (conv2): QuantizedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.02358696237206459, zero_point=109, padding=(1, 1))
        (bn2): Identity()
        (relu2): QuantizedReLU()
        (shortcut): LambdaLayer()
      )
      (1): BasicBlockCifar10(
        (conv1): QuantizedConvReLU2d(64, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.0076929363422095776, zero_point=0, padding=(1, 1))
        (bn1): Identity()
        (relu1): Identity()
        (conv2): QuantizedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.02710540033876896, zero_point=124, padding=(1, 1))
        (bn2): Identity()
        (relu2): QuantizedReLU()
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=tensor([0.0241]), zero_point=tensor([0])
            (observer): MovingAverageMinMaxObserver(min_val=0.0, max_val=6.156522750854492)
          )
        )
      )
      (2): BasicBlockCifar10(
        (conv1): QuantizedConvReLU2d(64, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.008539369329810143, zero_point=0, padding=(1, 1))
        (bn1): Identity()
        (relu1): Identity()
        (conv2): QuantizedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.06625250726938248, zero_point=92, padding=(1, 1))
        (bn2): Identity()
        (relu2): QuantizedReLU()
        (shortcut): Sequential(
          (observer): FakeQuantize(
            fake_quant_enabled=True, observer_enabled=True,            scale=tensor([0.0236]), zero_point=tensor([0])
            (observer): MovingAverageMinMaxObserver(min_val=0.0, max_val=6.006941795349121)
          )
        )
      )
    )
    (linear): QuantizedLinear(in_features=64, out_features=10, scale=0.10857795923948288, zero_point=69)
  )
)

It appears that some fakequantization nodes remain after converting.
shortcut is an empty nn.Sequential() layer (depending on the network arguments this gets overwritten to handle different strides)
But it running the model results in an error:
RuntimeError: Didn't find kernel to dispatch to for operator 'aten::sub'. Tried to look up kernel for dispatch key 'QuantizedCPUTensorId'. Registered dispatch keys are: [CUDATensorId, SparseCPUTensorId, VariableTensorId, CPUTensorId, SparseCUDATensorId]