code: def __init__(
      self, encoder_depth, num_classes=1, num_filters=32, dropout_2d=0.5
  ):
      super(UNet7, self).__init__()
      nf = num_filters
      self.name = "UNet7_" + str(encoder_depth) + "_nf" + str(nf)
      self.num_classes = num_classes
      self.dropout_2d = dropout_2d
  
      self.resnet, nbtm = create_resnet(encoder_depth)
  
      self.encoder1 = EncoderBlock(
          nn.Sequential(
              nn.Conv2d(
                  3, 64, kernel_size=7, stride=1, padding=3, bias=False
              ),
              nn.BatchNorm2d(64),
              nn.ReLU(inplace=True),
          ),
          64,
      )
      self.encoder2 = EncoderBlock(
          nn.Sequential(
              nn.MaxPool2d(kernel_size=2, stride=2),
              self.resnet.layer1,
          ),
          nbtm // 8,
      )
      self.encoder3 = EncoderBlock(self.resnet.layer2, nbtm // 4)
      self.encoder4 = EncoderBlock(self.resnet.layer3, nbtm // 2)
      self.encoder5 = EncoderBlock(self.resnet.layer4, nbtm)
  
      center_block = nn.Sequential(
          ConvBn2d(nbtm, nbtm, kernel_size=3, padding=1),
          nn.ReLU(inplace=True),
          ConvBn2d(nbtm, nbtm // 2, kernel_size=3, padding=1),
          nn.ReLU(inplace=True),
          # nn.MaxPool2d(kernel_size=2, stride=2) # remove
      )
      self.center = EncoderBlock(center_block, nbtm // 2)
  
      self.decoder5 = DecoderBlockV7(nbtm // 2, nbtm, nf * 16, nf * 2)
      self.decoder4 = DecoderBlockV7(nf * 2, nbtm // 2, nf * 8, nf * 2)
      self.decoder3 = DecoderBlockV7(nf * 2, nbtm // 4, nf * 4, nf * 2)
      self.decoder2 = DecoderBlockV7(nf * 2, nbtm // 8, nf * 2, nf * 2)
      self.decoder1 = DecoderBlockV7(nf * 2, 64, nf * 2, nf * 2)
  
      self.logit = nn.Sequential(
          nn.Conv2d(nf * 10, 64, kernel_size=3, padding=1),
          nn.ReLU(inplace=True),
          nn.Conv2d(64, 1, kernel_size=1, padding=0),
      )
  
      self.logit_image = nn.Sequential(
          nn.Linear(nbtm, 128),
          nn.ReLU(inplace=True),
          nn.Linear(128, 1),
      )
  
imports: from torch import nn
  from torch.nn import functional
  import torch
  from torchvision import models
  from torchvision.models import resnet34, resnet101, resnet50, resnet152
  import torchvision
label_desc: Use image node_id 1 to create a new UNet7 object from inherited base classes: nn with example: obj = UNet7(encoder_depth, num_classes, num_filters, dropout_2d)