quantized resnet18 model in PyTorch with quantized reference patterns

quantized: GraphModule(
  (conv1): ConvReLU2d(
    (0): Conv2d(
      3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)
      (weight_post_process): MinMaxObserver(min_val=-0.10300777107477188, max_val=0.09756611287593842)
    )
    (1): ReLU(inplace=True)
  )
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Module(
    (0): Module(
      (conv1): ConvReLU2d(
        (0): Conv2d(
          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
          (weight_post_process): MinMaxObserver(min_val=-0.2381705939769745, max_val=0.22953075170516968)
        )
        (1): ReLU(inplace=True)
      )
      (conv2): Conv2d(
        64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (weight_post_process): MinMaxObserver(min_val=-0.2629091143608093, max_val=0.2224341630935669)
      )
      (relu): ReLU(inplace=True)
    )
    (1): Module(
      (conv1): ConvReLU2d(
        (0): Conv2d(
          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
          (weight_post_process): MinMaxObserver(min_val=-0.22412416338920593, max_val=0.26422253251075745)
        )
        (1): ReLU(inplace=True)
      )
      (conv2): Conv2d(
        64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (weight_post_process): MinMaxObserver(min_val=-0.24510380625724792, max_val=0.2615947425365448)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (layer2): Module(
    (0): Module(
      (conv1): ConvReLU2d(
        (0): Conv2d(
          64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
          (weight_post_process): MinMaxObserver(min_val=-0.17348946630954742, max_val=0.16777494549751282)
        )
        (1): ReLU(inplace=True)
      )
      (conv2): Conv2d(
        128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (weight_post_process): MinMaxObserver(min_val=-0.19203202426433563, max_val=0.1656666100025177)
      )
      (downsample): Module(
        (0): Conv2d(
          64, 128, kernel_size=(1, 1), stride=(2, 2)
          (weight_post_process): MinMaxObserver(min_val=-0.4908600151538849, max_val=0.4047921299934387)
        )
      )
      (relu): ReLU(inplace=True)
    )
    (1): Module(
      (conv1): ConvReLU2d(
        (0): Conv2d(
          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
          (weight_post_process): MinMaxObserver(min_val=-0.20759394764900208, max_val=0.1722526252269745)
        )
        (1): ReLU(inplace=True)
      )
      (conv2): Conv2d(
        128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (weight_post_process): MinMaxObserver(min_val=-0.18452289700508118, max_val=0.18638639152050018)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (layer3): Module(
    (0): Module(
      (conv1): ConvReLU2d(
        (0): Conv2d(
          128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
          (weight_post_process): MinMaxObserver(min_val=-0.14486479759216309, max_val=0.13767831027507782)
        )
        (1): ReLU(inplace=True)
      )
      (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (weight_post_process): MinMaxObserver(min_val=-0.14476695656776428, max_val=0.14766506850719452)
      )
      (downsample): Module(
        (0): Conv2d(
          128, 256, kernel_size=(1, 1), stride=(2, 2)
          (weight_post_process): MinMaxObserver(min_val=-0.35111692547798157, max_val=0.3324059844017029)
        )
      )
      (relu): ReLU(inplace=True)
    )
    (1): Module(
      (conv1): ConvReLU2d(
        (0): Conv2d(
          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
          (weight_post_process): MinMaxObserver(min_val=-0.13645324110984802, max_val=0.13354922831058502)
        )
        (1): ReLU(inplace=True)
      )
      (conv2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (weight_post_process): MinMaxObserver(min_val=-0.14423039555549622, max_val=0.14707262814044952)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (layer4): Module(
    (0): Module(
      (conv1): ConvReLU2d(
        (0): Conv2d(
          256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
          (weight_post_process): MinMaxObserver(min_val=-0.10493732243776321, max_val=0.10505590587854385)
        )
        (1): ReLU(inplace=True)
      )
      (conv2): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (weight_post_process): MinMaxObserver(min_val=-0.10875571519136429, max_val=0.10566854476928711)
      )
      (downsample): Module(
        (0): Conv2d(
          256, 512, kernel_size=(1, 1), stride=(2, 2)
          (weight_post_process): MinMaxObserver(min_val=-0.28852152824401855, max_val=0.2584802806377411)
        )
      )
      (relu): ReLU(inplace=True)
    )
    (1): Module(
      (conv1): ConvReLU2d(
        (0): Conv2d(
          512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
          (weight_post_process): MinMaxObserver(min_val=-0.10482379794120789, max_val=0.11298386752605438)
        )
        (1): ReLU(inplace=True)
      )
      (conv2): Conv2d(
        512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (weight_post_process): MinMaxObserver(min_val=-0.11105507612228394, max_val=0.10828526318073273)
      )
      (relu): ReLU(inplace=True)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (fc): Linear(
    in_features=512, out_features=1000, bias=True
    (weight_post_process): MinMaxObserver(min_val=-0.044193997979164124, max_val=0.04419409856200218)
  )
)


def forward(self, x : torch.Tensor):
    conv1_input_scale_0 = self.conv1_input_scale_0
    conv1_input_zero_point_0 = self.conv1_input_zero_point_0
    quantize_per_tensor = torch.quantize_per_tensor(x, conv1_input_scale_0, conv1_input_zero_point_0, torch.qint8);  x = conv1_input_scale_0 = conv1_input_zero_point_0 = None
    dequantize = quantize_per_tensor.dequantize();  quantize_per_tensor = None
    conv1 = self.conv1(dequantize);  dequantize = None
    conv1_output_scale_0 = self.conv1_output_scale_0
    conv1_output_zero_point_0 = self.conv1_output_zero_point_0
    quantize_per_tensor_1 = torch.quantize_per_tensor(conv1, conv1_output_scale_0, conv1_output_zero_point_0, torch.qint8);  conv1 = conv1_output_scale_0 = conv1_output_zero_point_0 = None
    dequantize_1 = quantize_per_tensor_1.dequantize();  quantize_per_tensor_1 = None
    maxpool = self.maxpool(dequantize_1);  dequantize_1 = None
    maxpool_output_scale_0 = self.maxpool_output_scale_0
    maxpool_output_zero_point_0 = self.maxpool_output_zero_point_0
    quantize_per_tensor_2 = torch.quantize_per_tensor(maxpool, maxpool_output_scale_0, maxpool_output_zero_point_0, torch.qint8);  maxpool = maxpool_output_scale_0 = maxpool_output_zero_point_0 = None
    dequantize_2 = quantize_per_tensor_2.dequantize();  quantize_per_tensor_2 = None
    layer1_0_conv1 = getattr(self.layer1, "0").conv1(dequantize_2)
    layer1_0_conv1_output_scale_0 = self.layer1_0_conv1_output_scale_0
    layer1_0_conv1_output_zero_point_0 = self.layer1_0_conv1_output_zero_point_0
    quantize_per_tensor_3 = torch.quantize_per_tensor(layer1_0_conv1, layer1_0_conv1_output_scale_0, layer1_0_conv1_output_zero_point_0, torch.qint8);  layer1_0_conv1 = layer1_0_conv1_output_scale_0 = layer1_0_conv1_output_zero_point_0 = None
    dequantize_3 = quantize_per_tensor_3.dequantize();  quantize_per_tensor_3 = None
    layer1_0_conv2 = getattr(self.layer1, "0").conv2(dequantize_3);  dequantize_3 = None
    layer1_0_conv2_output_scale_0 = self.layer1_0_conv2_output_scale_0
    layer1_0_conv2_output_zero_point_0 = self.layer1_0_conv2_output_zero_point_0
    quantize_per_tensor_4 = torch.quantize_per_tensor(layer1_0_conv2, layer1_0_conv2_output_scale_0, layer1_0_conv2_output_zero_point_0, torch.qint8);  layer1_0_conv2 = layer1_0_conv2_output_scale_0 = layer1_0_conv2_output_zero_point_0 = None
    dequantize_4 = quantize_per_tensor_4.dequantize();  quantize_per_tensor_4 = None
    add = dequantize_4 + dequantize_2;  dequantize_4 = dequantize_2 = None
    layer1_0_relu_input_scale_0 = self.layer1_0_relu_input_scale_0
    layer1_0_relu_input_zero_point_0 = self.layer1_0_relu_input_zero_point_0
    quantize_per_tensor_5 = torch.quantize_per_tensor(add, layer1_0_relu_input_scale_0, layer1_0_relu_input_zero_point_0, torch.qint8);  add = layer1_0_relu_input_scale_0 = layer1_0_relu_input_zero_point_0 = None
    dequantize_5 = quantize_per_tensor_5.dequantize();  quantize_per_tensor_5 = None
    layer1_0_relu_1 = getattr(self.layer1, "0").relu(dequantize_5);  dequantize_5 = None
    layer1_0_relu_output_scale_0 = self.layer1_0_relu_output_scale_0
    layer1_0_relu_output_zero_point_0 = self.layer1_0_relu_output_zero_point_0
    quantize_per_tensor_6 = torch.quantize_per_tensor(layer1_0_relu_1, layer1_0_relu_output_scale_0, layer1_0_relu_output_zero_point_0, torch.qint8);  layer1_0_relu_1 = layer1_0_relu_output_scale_0 = layer1_0_relu_output_zero_point_0 = None
    dequantize_6 = quantize_per_tensor_6.dequantize();  quantize_per_tensor_6 = None
    layer1_1_conv1 = getattr(self.layer1, "1").conv1(dequantize_6)
    layer1_1_conv1_output_scale_0 = self.layer1_1_conv1_output_scale_0
    layer1_1_conv1_output_zero_point_0 = self.layer1_1_conv1_output_zero_point_0
    quantize_per_tensor_7 = torch.quantize_per_tensor(layer1_1_conv1, layer1_1_conv1_output_scale_0, layer1_1_conv1_output_zero_point_0, torch.qint8);  layer1_1_conv1 = layer1_1_conv1_output_scale_0 = layer1_1_conv1_output_zero_point_0 = None
    dequantize_7 = quantize_per_tensor_7.dequantize();  quantize_per_tensor_7 = None
    layer1_1_conv2 = getattr(self.layer1, "1").conv2(dequantize_7);  dequantize_7 = None
    layer1_1_conv2_output_scale_0 = self.layer1_1_conv2_output_scale_0
    layer1_1_conv2_output_zero_point_0 = self.layer1_1_conv2_output_zero_point_0
    quantize_per_tensor_8 = torch.quantize_per_tensor(layer1_1_conv2, layer1_1_conv2_output_scale_0, layer1_1_conv2_output_zero_point_0, torch.qint8);  layer1_1_conv2 = layer1_1_conv2_output_scale_0 = layer1_1_conv2_output_zero_point_0 = None
    dequantize_8 = quantize_per_tensor_8.dequantize();  quantize_per_tensor_8 = None
    add_1 = dequantize_8 + dequantize_6;  dequantize_8 = dequantize_6 = None
    layer1_1_relu_input_scale_0 = self.layer1_1_relu_input_scale_0
    layer1_1_relu_input_zero_point_0 = self.layer1_1_relu_input_zero_point_0
    quantize_per_tensor_9 = torch.quantize_per_tensor(add_1, layer1_1_relu_input_scale_0, layer1_1_relu_input_zero_point_0, torch.qint8);  add_1 = layer1_1_relu_input_scale_0 = layer1_1_relu_input_zero_point_0 = None
    dequantize_9 = quantize_per_tensor_9.dequantize();  quantize_per_tensor_9 = None
    layer1_1_relu_1 = getattr(self.layer1, "1").relu(dequantize_9);  dequantize_9 = None
    layer1_1_relu_output_scale_0 = self.layer1_1_relu_output_scale_0
    layer1_1_relu_output_zero_point_0 = self.layer1_1_relu_output_zero_point_0
    quantize_per_tensor_10 = torch.quantize_per_tensor(layer1_1_relu_1, layer1_1_relu_output_scale_0, layer1_1_relu_output_zero_point_0, torch.qint8);  layer1_1_relu_1 = layer1_1_relu_output_scale_0 = layer1_1_relu_output_zero_point_0 = None
    dequantize_10 = quantize_per_tensor_10.dequantize();  quantize_per_tensor_10 = None
    layer2_0_conv1 = getattr(self.layer2, "0").conv1(dequantize_10)
    layer2_0_conv1_output_scale_0 = self.layer2_0_conv1_output_scale_0
    layer2_0_conv1_output_zero_point_0 = self.layer2_0_conv1_output_zero_point_0
    quantize_per_tensor_11 = torch.quantize_per_tensor(layer2_0_conv1, layer2_0_conv1_output_scale_0, layer2_0_conv1_output_zero_point_0, torch.qint8);  layer2_0_conv1 = layer2_0_conv1_output_scale_0 = layer2_0_conv1_output_zero_point_0 = None
    dequantize_11 = quantize_per_tensor_11.dequantize();  quantize_per_tensor_11 = None
    layer2_0_conv2 = getattr(self.layer2, "0").conv2(dequantize_11);  dequantize_11 = None
    layer2_0_conv2_output_scale_0 = self.layer2_0_conv2_output_scale_0
    layer2_0_conv2_output_zero_point_0 = self.layer2_0_conv2_output_zero_point_0
    quantize_per_tensor_12 = torch.quantize_per_tensor(layer2_0_conv2, layer2_0_conv2_output_scale_0, layer2_0_conv2_output_zero_point_0, torch.qint8);  layer2_0_conv2 = layer2_0_conv2_output_scale_0 = layer2_0_conv2_output_zero_point_0 = None
    layer2_0_downsample_0 = getattr(getattr(self.layer2, "0").downsample, "0")(dequantize_10);  dequantize_10 = None
    layer2_0_downsample_0_output_scale_0 = self.layer2_0_downsample_0_output_scale_0
    layer2_0_downsample_0_output_zero_point_0 = self.layer2_0_downsample_0_output_zero_point_0
    quantize_per_tensor_13 = torch.quantize_per_tensor(layer2_0_downsample_0, layer2_0_downsample_0_output_scale_0, layer2_0_downsample_0_output_zero_point_0, torch.qint8);  layer2_0_downsample_0 = layer2_0_downsample_0_output_scale_0 = layer2_0_downsample_0_output_zero_point_0 = None
    dequantize_12 = quantize_per_tensor_12.dequantize();  quantize_per_tensor_12 = None
    dequantize_13 = quantize_per_tensor_13.dequantize();  quantize_per_tensor_13 = None
    add_2 = dequantize_12 + dequantize_13;  dequantize_12 = dequantize_13 = None
    layer2_0_relu_input_scale_0 = self.layer2_0_relu_input_scale_0
    layer2_0_relu_input_zero_point_0 = self.layer2_0_relu_input_zero_point_0
    quantize_per_tensor_14 = torch.quantize_per_tensor(add_2, layer2_0_relu_input_scale_0, layer2_0_relu_input_zero_point_0, torch.qint8);  add_2 = layer2_0_relu_input_scale_0 = layer2_0_relu_input_zero_point_0 = None
    dequantize_14 = quantize_per_tensor_14.dequantize();  quantize_per_tensor_14 = None
    layer2_0_relu_1 = getattr(self.layer2, "0").relu(dequantize_14);  dequantize_14 = None
    layer2_0_relu_output_scale_0 = self.layer2_0_relu_output_scale_0
    layer2_0_relu_output_zero_point_0 = self.layer2_0_relu_output_zero_point_0
    quantize_per_tensor_15 = torch.quantize_per_tensor(layer2_0_relu_1, layer2_0_relu_output_scale_0, layer2_0_relu_output_zero_point_0, torch.qint8);  layer2_0_relu_1 = layer2_0_relu_output_scale_0 = layer2_0_relu_output_zero_point_0 = None
    dequantize_15 = quantize_per_tensor_15.dequantize();  quantize_per_tensor_15 = None
    layer2_1_conv1 = getattr(self.layer2, "1").conv1(dequantize_15)
    layer2_1_conv1_output_scale_0 = self.layer2_1_conv1_output_scale_0
    layer2_1_conv1_output_zero_point_0 = self.layer2_1_conv1_output_zero_point_0
    quantize_per_tensor_16 = torch.quantize_per_tensor(layer2_1_conv1, layer2_1_conv1_output_scale_0, layer2_1_conv1_output_zero_point_0, torch.qint8);  layer2_1_conv1 = layer2_1_conv1_output_scale_0 = layer2_1_conv1_output_zero_point_0 = None
    dequantize_16 = quantize_per_tensor_16.dequantize();  quantize_per_tensor_16 = None
    layer2_1_conv2 = getattr(self.layer2, "1").conv2(dequantize_16);  dequantize_16 = None
    layer2_1_conv2_output_scale_0 = self.layer2_1_conv2_output_scale_0
    layer2_1_conv2_output_zero_point_0 = self.layer2_1_conv2_output_zero_point_0
    quantize_per_tensor_17 = torch.quantize_per_tensor(layer2_1_conv2, layer2_1_conv2_output_scale_0, layer2_1_conv2_output_zero_point_0, torch.qint8);  layer2_1_conv2 = layer2_1_conv2_output_scale_0 = layer2_1_conv2_output_zero_point_0 = None
    dequantize_17 = quantize_per_tensor_17.dequantize();  quantize_per_tensor_17 = None
    add_3 = dequantize_17 + dequantize_15;  dequantize_17 = dequantize_15 = None
    layer2_1_relu_input_scale_0 = self.layer2_1_relu_input_scale_0
    layer2_1_relu_input_zero_point_0 = self.layer2_1_relu_input_zero_point_0
    quantize_per_tensor_18 = torch.quantize_per_tensor(add_3, layer2_1_relu_input_scale_0, layer2_1_relu_input_zero_point_0, torch.qint8);  add_3 = layer2_1_relu_input_scale_0 = layer2_1_relu_input_zero_point_0 = None
    dequantize_18 = quantize_per_tensor_18.dequantize();  quantize_per_tensor_18 = None
    layer2_1_relu_1 = getattr(self.layer2, "1").relu(dequantize_18);  dequantize_18 = None
    layer2_1_relu_output_scale_0 = self.layer2_1_relu_output_scale_0
    layer2_1_relu_output_zero_point_0 = self.layer2_1_relu_output_zero_point_0
    quantize_per_tensor_19 = torch.quantize_per_tensor(layer2_1_relu_1, layer2_1_relu_output_scale_0, layer2_1_relu_output_zero_point_0, torch.qint8);  layer2_1_relu_1 = layer2_1_relu_output_scale_0 = layer2_1_relu_output_zero_point_0 = None
    dequantize_19 = quantize_per_tensor_19.dequantize();  quantize_per_tensor_19 = None
    layer3_0_conv1 = getattr(self.layer3, "0").conv1(dequantize_19)
    layer3_0_conv1_output_scale_0 = self.layer3_0_conv1_output_scale_0
    layer3_0_conv1_output_zero_point_0 = self.layer3_0_conv1_output_zero_point_0
    quantize_per_tensor_20 = torch.quantize_per_tensor(layer3_0_conv1, layer3_0_conv1_output_scale_0, layer3_0_conv1_output_zero_point_0, torch.qint8);  layer3_0_conv1 = layer3_0_conv1_output_scale_0 = layer3_0_conv1_output_zero_point_0 = None
    dequantize_20 = quantize_per_tensor_20.dequantize();  quantize_per_tensor_20 = None
    layer3_0_conv2 = getattr(self.layer3, "0").conv2(dequantize_20);  dequantize_20 = None
    layer3_0_conv2_output_scale_0 = self.layer3_0_conv2_output_scale_0
    layer3_0_conv2_output_zero_point_0 = self.layer3_0_conv2_output_zero_point_0
    quantize_per_tensor_21 = torch.quantize_per_tensor(layer3_0_conv2, layer3_0_conv2_output_scale_0, layer3_0_conv2_output_zero_point_0, torch.qint8);  layer3_0_conv2 = layer3_0_conv2_output_scale_0 = layer3_0_conv2_output_zero_point_0 = None
    layer3_0_downsample_0 = getattr(getattr(self.layer3, "0").downsample, "0")(dequantize_19);  dequantize_19 = None
    layer3_0_downsample_0_output_scale_0 = self.layer3_0_downsample_0_output_scale_0
    layer3_0_downsample_0_output_zero_point_0 = self.layer3_0_downsample_0_output_zero_point_0
    quantize_per_tensor_22 = torch.quantize_per_tensor(layer3_0_downsample_0, layer3_0_downsample_0_output_scale_0, layer3_0_downsample_0_output_zero_point_0, torch.qint8);  layer3_0_downsample_0 = layer3_0_downsample_0_output_scale_0 = layer3_0_downsample_0_output_zero_point_0 = None
    dequantize_21 = quantize_per_tensor_21.dequantize();  quantize_per_tensor_21 = None
    dequantize_22 = quantize_per_tensor_22.dequantize();  quantize_per_tensor_22 = None
    add_4 = dequantize_21 + dequantize_22;  dequantize_21 = dequantize_22 = None
    layer3_0_relu_input_scale_0 = self.layer3_0_relu_input_scale_0
    layer3_0_relu_input_zero_point_0 = self.layer3_0_relu_input_zero_point_0
    quantize_per_tensor_23 = torch.quantize_per_tensor(add_4, layer3_0_relu_input_scale_0, layer3_0_relu_input_zero_point_0, torch.qint8);  add_4 = layer3_0_relu_input_scale_0 = layer3_0_relu_input_zero_point_0 = None
    dequantize_23 = quantize_per_tensor_23.dequantize();  quantize_per_tensor_23 = None
    layer3_0_relu_1 = getattr(self.layer3, "0").relu(dequantize_23);  dequantize_23 = None
    layer3_0_relu_output_scale_0 = self.layer3_0_relu_output_scale_0
    layer3_0_relu_output_zero_point_0 = self.layer3_0_relu_output_zero_point_0
    quantize_per_tensor_24 = torch.quantize_per_tensor(layer3_0_relu_1, layer3_0_relu_output_scale_0, layer3_0_relu_output_zero_point_0, torch.qint8);  layer3_0_relu_1 = layer3_0_relu_output_scale_0 = layer3_0_relu_output_zero_point_0 = None
    dequantize_24 = quantize_per_tensor_24.dequantize();  quantize_per_tensor_24 = None
    layer3_1_conv1 = getattr(self.layer3, "1").conv1(dequantize_24)
    layer3_1_conv1_output_scale_0 = self.layer3_1_conv1_output_scale_0
    layer3_1_conv1_output_zero_point_0 = self.layer3_1_conv1_output_zero_point_0
    quantize_per_tensor_25 = torch.quantize_per_tensor(layer3_1_conv1, layer3_1_conv1_output_scale_0, layer3_1_conv1_output_zero_point_0, torch.qint8);  layer3_1_conv1 = layer3_1_conv1_output_scale_0 = layer3_1_conv1_output_zero_point_0 = None
    dequantize_25 = quantize_per_tensor_25.dequantize();  quantize_per_tensor_25 = None
    layer3_1_conv2 = getattr(self.layer3, "1").conv2(dequantize_25);  dequantize_25 = None
    layer3_1_conv2_output_scale_0 = self.layer3_1_conv2_output_scale_0
    layer3_1_conv2_output_zero_point_0 = self.layer3_1_conv2_output_zero_point_0
    quantize_per_tensor_26 = torch.quantize_per_tensor(layer3_1_conv2, layer3_1_conv2_output_scale_0, layer3_1_conv2_output_zero_point_0, torch.qint8);  layer3_1_conv2 = layer3_1_conv2_output_scale_0 = layer3_1_conv2_output_zero_point_0 = None
    dequantize_26 = quantize_per_tensor_26.dequantize();  quantize_per_tensor_26 = None
    add_5 = dequantize_26 + dequantize_24;  dequantize_26 = dequantize_24 = None
    layer3_1_relu_input_scale_0 = self.layer3_1_relu_input_scale_0
    layer3_1_relu_input_zero_point_0 = self.layer3_1_relu_input_zero_point_0
    quantize_per_tensor_27 = torch.quantize_per_tensor(add_5, layer3_1_relu_input_scale_0, layer3_1_relu_input_zero_point_0, torch.qint8);  add_5 = layer3_1_relu_input_scale_0 = layer3_1_relu_input_zero_point_0 = None
    dequantize_27 = quantize_per_tensor_27.dequantize();  quantize_per_tensor_27 = None
    layer3_1_relu_1 = getattr(self.layer3, "1").relu(dequantize_27);  dequantize_27 = None
    layer3_1_relu_output_scale_0 = self.layer3_1_relu_output_scale_0
    layer3_1_relu_output_zero_point_0 = self.layer3_1_relu_output_zero_point_0
    quantize_per_tensor_28 = torch.quantize_per_tensor(layer3_1_relu_1, layer3_1_relu_output_scale_0, layer3_1_relu_output_zero_point_0, torch.qint8);  layer3_1_relu_1 = layer3_1_relu_output_scale_0 = layer3_1_relu_output_zero_point_0 = None
    dequantize_28 = quantize_per_tensor_28.dequantize();  quantize_per_tensor_28 = None
    layer4_0_conv1 = getattr(self.layer4, "0").conv1(dequantize_28)
    layer4_0_conv1_output_scale_0 = self.layer4_0_conv1_output_scale_0
    layer4_0_conv1_output_zero_point_0 = self.layer4_0_conv1_output_zero_point_0
    quantize_per_tensor_29 = torch.quantize_per_tensor(layer4_0_conv1, layer4_0_conv1_output_scale_0, layer4_0_conv1_output_zero_point_0, torch.qint8);  layer4_0_conv1 = layer4_0_conv1_output_scale_0 = layer4_0_conv1_output_zero_point_0 = None
    dequantize_29 = quantize_per_tensor_29.dequantize();  quantize_per_tensor_29 = None
    layer4_0_conv2 = getattr(self.layer4, "0").conv2(dequantize_29);  dequantize_29 = None
    layer4_0_conv2_output_scale_0 = self.layer4_0_conv2_output_scale_0
    layer4_0_conv2_output_zero_point_0 = self.layer4_0_conv2_output_zero_point_0
    quantize_per_tensor_30 = torch.quantize_per_tensor(layer4_0_conv2, layer4_0_conv2_output_scale_0, layer4_0_conv2_output_zero_point_0, torch.qint8);  layer4_0_conv2 = layer4_0_conv2_output_scale_0 = layer4_0_conv2_output_zero_point_0 = None
    layer4_0_downsample_0 = getattr(getattr(self.layer4, "0").downsample, "0")(dequantize_28);  dequantize_28 = None
    layer4_0_downsample_0_output_scale_0 = self.layer4_0_downsample_0_output_scale_0
    layer4_0_downsample_0_output_zero_point_0 = self.layer4_0_downsample_0_output_zero_point_0
    quantize_per_tensor_31 = torch.quantize_per_tensor(layer4_0_downsample_0, layer4_0_downsample_0_output_scale_0, layer4_0_downsample_0_output_zero_point_0, torch.qint8);  layer4_0_downsample_0 = layer4_0_downsample_0_output_scale_0 = layer4_0_downsample_0_output_zero_point_0 = None
    dequantize_30 = quantize_per_tensor_30.dequantize();  quantize_per_tensor_30 = None
    dequantize_31 = quantize_per_tensor_31.dequantize();  quantize_per_tensor_31 = None
    add_6 = dequantize_30 + dequantize_31;  dequantize_30 = dequantize_31 = None
    layer4_0_relu_input_scale_0 = self.layer4_0_relu_input_scale_0
    layer4_0_relu_input_zero_point_0 = self.layer4_0_relu_input_zero_point_0
    quantize_per_tensor_32 = torch.quantize_per_tensor(add_6, layer4_0_relu_input_scale_0, layer4_0_relu_input_zero_point_0, torch.qint8);  add_6 = layer4_0_relu_input_scale_0 = layer4_0_relu_input_zero_point_0 = None
    dequantize_32 = quantize_per_tensor_32.dequantize();  quantize_per_tensor_32 = None
    layer4_0_relu_1 = getattr(self.layer4, "0").relu(dequantize_32);  dequantize_32 = None
    layer4_0_relu_output_scale_0 = self.layer4_0_relu_output_scale_0
    layer4_0_relu_output_zero_point_0 = self.layer4_0_relu_output_zero_point_0
    quantize_per_tensor_33 = torch.quantize_per_tensor(layer4_0_relu_1, layer4_0_relu_output_scale_0, layer4_0_relu_output_zero_point_0, torch.qint8);  layer4_0_relu_1 = layer4_0_relu_output_scale_0 = layer4_0_relu_output_zero_point_0 = None
    dequantize_33 = quantize_per_tensor_33.dequantize();  quantize_per_tensor_33 = None
    layer4_1_conv1 = getattr(self.layer4, "1").conv1(dequantize_33)
    layer4_1_conv1_output_scale_0 = self.layer4_1_conv1_output_scale_0
    layer4_1_conv1_output_zero_point_0 = self.layer4_1_conv1_output_zero_point_0
    quantize_per_tensor_34 = torch.quantize_per_tensor(layer4_1_conv1, layer4_1_conv1_output_scale_0, layer4_1_conv1_output_zero_point_0, torch.qint8);  layer4_1_conv1 = layer4_1_conv1_output_scale_0 = layer4_1_conv1_output_zero_point_0 = None
    dequantize_34 = quantize_per_tensor_34.dequantize();  quantize_per_tensor_34 = None
    layer4_1_conv2 = getattr(self.layer4, "1").conv2(dequantize_34);  dequantize_34 = None
    layer4_1_conv2_output_scale_0 = self.layer4_1_conv2_output_scale_0
    layer4_1_conv2_output_zero_point_0 = self.layer4_1_conv2_output_zero_point_0
    quantize_per_tensor_35 = torch.quantize_per_tensor(layer4_1_conv2, layer4_1_conv2_output_scale_0, layer4_1_conv2_output_zero_point_0, torch.qint8);  layer4_1_conv2 = layer4_1_conv2_output_scale_0 = layer4_1_conv2_output_zero_point_0 = None
    dequantize_35 = quantize_per_tensor_35.dequantize();  quantize_per_tensor_35 = None
    add_7 = dequantize_35 + dequantize_33;  dequantize_35 = dequantize_33 = None
    layer4_1_relu_input_scale_0 = self.layer4_1_relu_input_scale_0
    layer4_1_relu_input_zero_point_0 = self.layer4_1_relu_input_zero_point_0
    quantize_per_tensor_36 = torch.quantize_per_tensor(add_7, layer4_1_relu_input_scale_0, layer4_1_relu_input_zero_point_0, torch.qint8);  add_7 = layer4_1_relu_input_scale_0 = layer4_1_relu_input_zero_point_0 = None
    dequantize_36 = quantize_per_tensor_36.dequantize();  quantize_per_tensor_36 = None
    layer4_1_relu_1 = getattr(self.layer4, "1").relu(dequantize_36);  dequantize_36 = None
    layer4_1_relu_output_scale_0 = self.layer4_1_relu_output_scale_0
    layer4_1_relu_output_zero_point_0 = self.layer4_1_relu_output_zero_point_0
    quantize_per_tensor_37 = torch.quantize_per_tensor(layer4_1_relu_1, layer4_1_relu_output_scale_0, layer4_1_relu_output_zero_point_0, torch.qint8);  layer4_1_relu_1 = layer4_1_relu_output_scale_0 = layer4_1_relu_output_zero_point_0 = None
    dequantize_37 = quantize_per_tensor_37.dequantize();  quantize_per_tensor_37 = None
    avgpool = self.avgpool(dequantize_37);  dequantize_37 = None
    avgpool_output_scale_0 = self.avgpool_output_scale_0
    avgpool_output_zero_point_0 = self.avgpool_output_zero_point_0
    quantize_per_tensor_38 = torch.quantize_per_tensor(avgpool, avgpool_output_scale_0, avgpool_output_zero_point_0, torch.qint8);  avgpool = avgpool_output_scale_0 = avgpool_output_zero_point_0 = None
    flatten = torch.flatten(quantize_per_tensor_38, 1);  quantize_per_tensor_38 = None
    dequantize_38 = flatten.dequantize();  flatten = None
    fc = self.fc(dequantize_38);  dequantize_38 = None
    fc_output_scale_0 = self.fc_output_scale_0
    fc_output_zero_point_0 = self.fc_output_zero_point_0
    quantize_per_tensor_39 = torch.quantize_per_tensor(fc, fc_output_scale_0, fc_output_zero_point_0, torch.qint8);  fc = fc_output_scale_0 = fc_output_zero_point_0 = None
    dequantize_39 = quantize_per_tensor_39.dequantize();  quantize_per_tensor_39 = None
    return dequantize_39