code: def forward(self, inputs):
      conv1 = self.conv1(inputs)  # 16*512*512
      maxpool1 = self.maxpool1(conv1)  # 16*256*256
  
      conv2 = self.conv2(maxpool1)  # 32*256*256
      maxpool2 = self.maxpool2(conv2)  # 32*128*128
  
      conv3 = self.conv3(maxpool2)  # 64*128*128
      maxpool3 = self.maxpool3(conv3)  # 64*64*64
  
      conv4 = self.conv4(maxpool3)  # 128*64*64
      maxpool4 = self.maxpool4(conv4)  # 128*32*32
  
      center = self.center(maxpool4)  # 256*32*32
      cls_branch = self.cls(center).squeeze()
      up4 = self.up_concat4(center, conv4)  # 128*64*64
      up3 = self.up_concat3(up4, conv3)  # 64*128*128
      up2 = self.up_concat2(up3, conv2)  # 32*256*256
      up1 = self.up_concat1(up2, conv1)  # 16*512*512
  
      final_1 = self.final_1(up1)
      # final_2 = self.final_2(up1)
      # final_3 = self.final_3(up1)
  
      # return F.log_softmax(final_1, dim=1), cls_branch
      return final_1
  
imports: import torch.nn
  import torch.nn.functional
  from torch.nn import init
  from .Unet_nested_layers import unetConv2, unetUp, unetConv2_dilation
label_desc: Use image node_id 2 for calling the UNet obj's underlying member method code with example usage: obj.forward(inputs) and returns: final_1