          var_x = Conv2D(3, kernel_size=5, padding="same", activation="sigmoid")(var)
          outputs = [var_x]
          # Mask
          if self.config.get("mask_type", None):
              var_y = Conv2D(1, kernel_size=5, padding="same", activation="sigmoid")(var)
              outputs.append(var_y)
          return KerasModel(input_, outputs=outputs)
 
</s> remove         var = input_
        var = self.blocks.upscale(var, self.encoder_dim)
        var = self.blocks.upscale(var, self.encoder_dim // 2)
        var = self.blocks.upscale(var, self.encoder_dim // 4)

 </s> add  </s> remove             var_y = Conv2D(1, kernel_size=5, padding="same", activation="sigmoid")(var_x)
 </s> add             var_y = input_
            var_y = self.blocks.upscale(var_y, 512)
            var_y = self.blocks.upscale(var_y, 256)
            var_y = self.blocks.upscale(var_y, 128)
            var_y = self.blocks.upscale(var_y, 64)
            var_y = Conv2D(1, kernel_size=5, padding="same", activation="sigmoid")(var_y) </s> remove         var_x = Conv2D(3, kernel_size=5, padding="same", activation="sigmoid")(var)
 </s> add         var_x = input_
        var_x = self.blocks.upscale(var_x, self.encoder_dim)
        var_x = self.blocks.upscale(var_x, self.encoder_dim // 2)
        var_x = self.blocks.upscale(var_x, self.encoder_dim // 4)
        var_x = Conv2D(3, kernel_size=5, padding="same", activation="sigmoid")(var_x)
            var_y = input_
            var_y = self.blocks.upscale(var_y, self.encoder_dim)
            var_y = self.blocks.upscale(var_y, self.encoder_dim // 2)
            var_y = self.blocks.upscale(var_y, self.encoder_dim // 4)
            var_y = Conv2D(1, kernel_size=5, padding="same", activation="sigmoid")(var_y)
          var_x = Conv2D(3, kernel_size=5, padding="same", activation="sigmoid")(var)
          outputs = [var_x]
          # Mask
          if self.config.get("mask_type", None):
            var_y = input_
            var_y = self.blocks.upscale(var_y, self.encoder_dim)
            var_y = self.blocks.upscale(var_y, self.encoder_dim // 2)
            var_y = self.blocks.upscale(var_y, self.encoder_dim // 4)
            var_y = Conv2D(1, kernel_size=5, padding="same", activation="sigmoid")(var_y)
              outputs.append(var_y)
          return KerasModel(input_, outputs=outputs)
keep keep keep keep replace keep keep
 Correct the mask decoders for dfl_h128 and IAE 
plugins/train/model/dfl_h128.py