code: def __init__(
      self, encoder_depth, num_classes=1, num_filters=32, dropout_2d=0.5
  ):
      super(UNetResNetV5, self).__init__()
      self.name = "UNetResNetV5_" + str(encoder_depth)
      self.num_classes = num_classes
      self.dropout_2d = dropout_2d
  
      self.resnet, bottom_channel_nr = create_resnet(encoder_depth)
  
      self.encoder1 = EncoderBlock(
          nn.Sequential(
              self.resnet.conv1, self.resnet.bn1, self.resnet.relu
          ),
          num_filters * 2,
      )
      self.encoder2 = EncoderBlock(
          self.resnet.layer1, bottom_channel_nr // 8
      )
      self.encoder3 = EncoderBlock(
          self.resnet.layer2, bottom_channel_nr // 4
      )
      self.encoder4 = EncoderBlock(
          self.resnet.layer3, bottom_channel_nr // 2
      )
      self.encoder5 = EncoderBlock(
          self.resnet.layer4, bottom_channel_nr
      )
  
      center_block = nn.Sequential(
          ConvBn2d(
              bottom_channel_nr,
              bottom_channel_nr,
              kernel_size=3,
              padding=1,
          ),
          nn.ReLU(inplace=True),
          ConvBn2d(
              bottom_channel_nr,
              bottom_channel_nr // 2,
              kernel_size=3,
              padding=1,
          ),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(kernel_size=2, stride=2),
      )
      self.center = EncoderBlock(center_block, bottom_channel_nr // 2)
  
      self.decoder5 = DecoderBlockV5(
          bottom_channel_nr // 2,
          bottom_channel_nr,
          num_filters * 16,
          64,
      )
      self.decoder4 = DecoderBlockV5(
          64, bottom_channel_nr // 2, num_filters * 8, 64
      )
      self.decoder3 = DecoderBlockV5(
          64, bottom_channel_nr // 4, num_filters * 4, 64
      )
      self.decoder2 = DecoderBlockV5(
          64, bottom_channel_nr // 8, num_filters * 2, 64
      )
      self.decoder1 = DecoderBlockV5(64, 0, num_filters, 64)
  
      self.logit = nn.Sequential(
          nn.Conv2d(320, 64, kernel_size=3, padding=1),
          nn.ReLU(inplace=True),
          nn.Conv2d(64, 1, kernel_size=1, padding=0),
      )
  
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 UNetResNetV5 object from inherited base classes: nn with example: obj = UNetResNetV5(encoder_depth, num_classes, num_filters, dropout_2d)