code: def __init__(
      self,
      block,
      layers,
      num_classes=1000,
      zero_init_residual=False,
      groups=1,
      width_per_group=64,
      replace_stride_with_dilation=None,
      norm_layer=None,
      bit_width=8,
  ):
      super().__init__()
      if norm_layer is None:
          norm_layer = nn.BatchNorm2d
      self._norm_layer = norm_layer
  
      self.inplanes = 64
      self.dilation = 1
      if replace_stride_with_dilation is None:
          # each element in the tuple indicates if we should replace
          # the 2x2 stride with a dilated convolution instead
          replace_stride_with_dilation = [False, False, False]
      if len(replace_stride_with_dilation) != 3:
          raise ValueError(
              "replace_stride_with_dilation should be None "
              "or a 3-element tuple, got {}".format(
                  replace_stride_with_dilation
              )
          )
      self.groups = groups
      self.base_width = width_per_group
      self.conv1 = qnn.QuantConv2d(
          3,
          self.inplanes,
          kernel_size=7,
          stride=2,
          padding=3,
          bias=False,
      )
      self.bn1 = norm_layer(self.inplanes)
      self.relu = make_PACT_relu()
      self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
      self.layer1 = self._make_layer(
          block, 64, layers[0], bit_width=bit_width
      )
      self.layer2 = self._make_layer(
          block,
          128,
          layers[1],
          stride=2,
          dilate=replace_stride_with_dilation[0],
          bit_width=bit_width,
      )
      self.layer3 = self._make_layer(
          block,
          256,
          layers[2],
          stride=2,
          dilate=replace_stride_with_dilation[1],
          bit_width=bit_width,
      )
      self.layer4 = self._make_layer(
          block,
          512,
          layers[3],
          stride=2,
          dilate=replace_stride_with_dilation[2],
          bit_width=bit_width,
      )
      self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
      self.fc = nn.Linear(512 * block.expansion, num_classes)
  
      for m in self.modules():
          if isinstance(
              m, nn.Conv2d
          ):  # qnn.QuantConv2d includes nn.Conv2d inside.
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
          elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
              nn.init.constant_(m.weight, 1)
              nn.init.constant_(m.bias, 0)
  
      # Zero-initialize the last BN in each residual branch,
      # so that the residual branch starts with zeros, and each residual block behaves like an identity.
      # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
      if zero_init_residual:
          for m in self.modules():
              if isinstance(m, Bottleneck):
                  nn.init.constant_(m.bn3.weight, 0)
              elif isinstance(m, BasicBlock):
                  nn.init.constant_(m.bn2.weight, 0)
  
imports: import os
  from pathlib import Path
  import brevitas.nn
  import torch
  import torch.nn
  from pact import PACTReLU
  from torchvision import datasets, models
  from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor
label_desc: Use image node_id 1 to create a new ResNet_QAT_Xb object from inherited base classes: nn with example: obj = ResNet_QAT_Xb(block, layers, num_classes, zero_init_residual, groups, width_per_group, replace_stride_with_dilation, norm_layer, bit_width)