code: def forward(self, inputs: paddle.Tensor):
      y = self.backbone(inputs)
      feature = y
      y = paddle.flatten(y, 1)
  
      y1 = self.fc_spinal_layer1(y[:, 0 : self.half_in_size])
      y2 = self.fc_spinal_layer2(
          paddle.concat(
              [y[:, self.half_in_size : 2 * self.half_in_size], y1],
              axis=1,
          )
      )
      y3 = self.fc_spinal_layer3(
          paddle.concat([y[:, 0 : self.half_in_size], y2], axis=1)
      )
      y4 = self.fc_spinal_layer4(
          paddle.concat(
              [y[:, self.half_in_size : 2 * self.half_in_size], y3],
              axis=1,
          )
      )
  
      y = paddle.concat([y1, y2, y3, y4], axis=1)
  
      y = self.fc_out(y)
      return y, feature
  
imports: import os
  from typing import Union
  import numpy
  import paddle
  import paddle.nn
  import paddlehub.vision.transforms
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.cv_module import ImageClassifierModule
  import paddle
  from paddle import nn
label_desc: Use image node_id 3 for calling the SpinalNet_VGG16 obj's underlying member method code with example usage: obj.forward(inputs) and returns: y,  feature