code: def __init__(self, backbone="resnet50"):
      super().__init__()
      if backbone == "resnet18":
          backbone = resnet18(pretrained=True)
          self.out_channels = [256, 512, 512, 256, 256, 128]
      elif backbone == "resnet34":
          backbone = resnet34(pretrained=True)
          self.out_channels = [256, 512, 512, 256, 256, 256]
      elif backbone == "resnet50":
          backbone = resnet50(pretrained=True)
          self.out_channels = [1024, 512, 512, 256, 256, 256]
      elif backbone == "resnet101":
          backbone = resnet101(pretrained=True)
          self.out_channels = [1024, 512, 512, 256, 256, 256]
      else:  # backbone == 'resnet152':
          backbone = resnet152(pretrained=True)
          self.out_channels = [1024, 512, 512, 256, 256, 256]
  
      self.feature_extractor = nn.Sequential(
          *list(backbone.children())[:7]
      )
  
      conv4_block1 = self.feature_extractor[-1][0]
  
      conv4_block1.conv1.stride = (1, 1)
      conv4_block1.conv2.stride = (1, 1)
      conv4_block1.downsample[0].stride = (1, 1)
  
imports: import torch
  import torch.nn
  from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet101, resnet152
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 1 to create a new ResNet object from inherited base classes: nn with example: obj = ResNet(backbone)