code: def forward(self, inputs):
      # column : 0
      X_00 = self.conv00(inputs)  # 16*512*512
      maxpool0 = self.maxpool0(X_00)  # 16*256*256
      X_10 = self.conv10(maxpool0)  # 32*256*256
      maxpool1 = self.maxpool1(X_10)  # 32*128*128
      X_20 = self.conv20(maxpool1)  # 64*128*128
      maxpool2 = self.maxpool2(X_20)  # 64*64*64
      X_30 = self.conv30(maxpool2)  # 128*64*64
      maxpool3 = self.maxpool3(X_30)  # 128*32*32
      X_40 = self.conv40(maxpool3)  # 256*32*32
      cls_branch = self.cls(X_40).squeeze()
      # column : 1
      X_01 = self.up_concat01(X_10, X_00)
      X_11 = self.up_concat11(X_20, X_10)
      X_21 = self.up_concat21(X_30, X_20)
      X_31 = self.up_concat31(X_40, X_30)
      # column : 2
      X_02 = self.up_concat02(X_11, X_00, X_01)
      X_12 = self.up_concat12(X_21, X_10, X_11)
      X_22 = self.up_concat22(X_31, X_20, X_21)
      # column : 3
      X_03 = self.up_concat03(X_12, X_00, X_01, X_02)
      X_13 = self.up_concat13(X_22, X_10, X_11, X_12)
      # column : 4
      X_04 = self.up_concat04(X_13, X_00, X_01, X_02, X_03)
  
      # final layer
      final_1 = self.final_1(X_01)
      final_2 = self.final_2(X_02)
      final_3 = self.final_3(X_03)
      final_4 = self.final_4(X_04)
  
      final = (final_1 + final_2 + final_3 + final_4) / 4
  
      if self.is_ds:
          # return F.log_softmax(final, dim=1), cls_branch
          return final
      else:
          # return F.log_softmax(final_4), cls_branch
          return final_4
  
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_Nested obj's underlying member method code with example usage: obj.forward(inputs) and returns: final, final_4