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,
  ):
      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 "
              f"or a 3-element tuple, got {replace_stride_with_dilation}"
          )
      self.groups = groups
      self.base_width = width_per_group
      self.conv1 = nn.Conv2d(
          3,
          self.inplanes,
          kernel_size=7,
          stride=2,
          padding=3,
          bias=False,
      )
      self.bn1 = norm_layer(self.inplanes)
      self.relu = nn.ReLU(inplace=True)
      self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
      self.layer1 = self._make_layer(block, 64, layers[0])
      self.layer2 = self._make_layer(
          block,
          128,
          layers[1],
          stride=2,
          dilate=replace_stride_with_dilation[0],
      )
      self.layer3 = self._make_layer(
          block,
          256,
          layers[2],
          stride=2,
          dilate=replace_stride_with_dilation[1],
      )
      self.layer4 = self._make_layer(
          block,
          512,
          layers[3],
          stride=2,
          dilate=replace_stride_with_dilation[2],
      )
      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):
              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: from collections import OrderedDict
  import torch
  from torch import nn
  from torch.jit.annotations import Dict
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new ResNet object from inherited base classes: nn with example: obj = ResNet(block, layers, num_classes, zero_init_residual, groups, width_per_group, replace_stride_with_dilation, norm_layer)