code: def __init__(self, block, layers, num_classes=1000, stride=8):
      self.inplanes = 128
      super().__init__()
      self.conv1 = nn.Sequential(
          nn.Conv2d(
              3, 64, kernel_size=3, stride=2, padding=1, bias=False
          ),
          nn.BatchNorm2d(64),
          nn.ReLU(inplace=True),
          nn.Conv2d(
              64, 64, kernel_size=3, stride=1, padding=1, bias=False
          ),
          nn.BatchNorm2d(64),
          nn.ReLU(inplace=True),
          nn.Conv2d(
              64, 128, kernel_size=3, stride=1, padding=1, bias=False
          ),
      )
  
      self.bn1 = nn.BatchNorm2d(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)
  
      if stride == 16:
          self.layer3 = self._make_layer(
              block, 256, layers[2], stride=2
          )
          self.layer4 = self._make_layer(
              block,
              512,
              layers[3],
              stride=1,
              dilation=2,
              grids=[1, 2, 4],
          )
      elif stride == 8:
          self.layer3 = self._make_layer(
              block, 256, layers[2], stride=1, dilation=2
          )
          self.layer4 = self._make_layer(
              block,
              512,
              layers[3],
              stride=1,
              dilation=4,
              grids=[1, 2, 4],
          )
  
      self.avgpool = nn.AvgPool2d(7, stride=1)
      self.fc = nn.Linear(512 * block.expansion, num_classes)
  
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
              m.weight.data.normal_(0, math.sqrt(2.0 / n))
          elif isinstance(m, _BatchNorm):
              m.weight.data.fill_(1)
              if m.bias is not None:
                  m.bias.data.zero_()
  
imports: import math
  import torch.utils.model_zoo
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.batchnorm import _BatchNorm
  from .None import settings
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, stride)