class_docstr: Unet utilizing dilation
class_docstr_tok: ["Unet","utilizing","dilation"]
code: def forward(self, x):
      x1 = self.Conv0(x)
      down1 = self.down1(x1)
      down2 = self.down2(down1)
      down3 = self.down3(down2)
      down4 = self.down4(down3)
      down5 = self.down5(down4)
      down5 = self.tran0(down5)
      down5 = self.db0(down5)
  
      ## TODO Problem here:
      # self.up1(down5).data.shape    =>  torch.Size([2, 96, 64, 44])
      #                       -- MISMATCH WITH --
      # down4.data.shape              =>  torch.Size([2, 96, 64, 43])
  
      up1 = self.act(
          self.bn1(
              self.conv1(
                  torch.cat([self.db1(self.up1(down5)), down4], dim=1)
              )
          )
      )
  
      up2 = self.act(
          self.bn2(
              self.conv2(
                  torch.cat([self.db2(self.up2(up1)), down3], dim=1)
              )
          )
      )
  
      up3 = self.act(
          self.bn3(
              self.conv3(
                  torch.cat([self.db3(self.up3(up2)), down2], dim=1)
              )
          )
      )
  
      up4 = self.act(
          self.bn4(
              self.conv4(
                  torch.cat([self.db4(self.up4(up3)), down1], dim=1)
              )
          )
      )
  
      up5 = self.up5(up4)
      # up5=self.conv5(up5)
  
      # return self.clss(self.conv5(up5))
      return self.conv5(up5)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the UNetDilated obj's underlying member method code with example usage: obj.forward(x) and returns: self