code: def forward(self, x):
      e1 = self.encoder1(x)  # ; print('e1:', e1.size())
      e2 = self.encoder2(e1)  # ; print('e2:', e2.size())
      e3 = self.encoder3(e2)  # ; print('e3:', e3.size())
      e4 = self.encoder4(e3)  # ; print('e4:', e4.size())
      e5 = self.encoder5(e4)  # ; print('e5:', e5.size())
  
      center = self.center(e5)  # ; print('center:', center.size())
  
      d5 = self.decoder5(
          center, e5, upsample=False
      )  # ; print('d5:', d5.size())
      d4 = self.decoder4(d5, e4)  # ; print('d4:', d4.size())
      d3 = self.decoder3(d4, e3)  # ; print('d3:', d3.size())
      d2 = self.decoder2(d3, e2)  # ; print('d2:', d2.size())
      d1 = self.decoder1(
          torch.cat([d2, e1], 1), x
      )  # ; print('d1:', d1.size())
  
      f = torch.cat(
          [
              d1,
              F.interpolate(
                  d2,
                  scale_factor=2,
                  mode="bilinear",
                  align_corners=False,
              ),
              F.interpolate(
                  d3,
                  scale_factor=4,
                  mode="bilinear",
                  align_corners=False,
              ),
              F.interpolate(
                  d4,
                  scale_factor=8,
                  mode="bilinear",
                  align_corners=False,
              ),
              F.interpolate(
                  d5,
                  scale_factor=16,
                  mode="bilinear",
                  align_corners=False,
              ),
          ],
          1,
      )
  
      f = F.dropout2d(f, p=self.dropout_2d)
  
      # empty mask classifier
      img_f = F.adaptive_avg_pool2d(e5, 1).view(x.size(0), -1)
      img_f = F.dropout(img_f, p=0.5, training=self.training)
      img_logit = self.logit_image(img_f).view(-1)
  
      return self.logit(f), img_logit
  
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 2 for calling the UNet8 obj's underlying member method code with example usage: obj.forward(x) and returns: self,  img_logit