code: def build_vgg_backbone(inputs):
      x = make_vgg_block(
          inputs,
          filters=64,
          n=0,
          pooling=False,
          prefix="basenet.slice1",
      )
      x = make_vgg_block(
          x, filters=64, n=3, pooling=True, prefix="basenet.slice1"
      )
      x = make_vgg_block(
          x, filters=128, n=7, pooling=False, prefix="basenet.slice1"
      )
      x = make_vgg_block(
          x, filters=128, n=10, pooling=True, prefix="basenet.slice1"
      )
      x = make_vgg_block(
          x, filters=256, n=14, pooling=False, prefix="basenet.slice2"
      )
      x = make_vgg_block(
          x, filters=256, n=17, pooling=False, prefix="basenet.slice2"
      )
      x = make_vgg_block(
          x, filters=256, n=20, pooling=True, prefix="basenet.slice3"
      )
      x = make_vgg_block(
          x, filters=512, n=24, pooling=False, prefix="basenet.slice3"
      )
      x = make_vgg_block(
          x, filters=512, n=27, pooling=False, prefix="basenet.slice3"
      )
      x = make_vgg_block(
          x, filters=512, n=30, pooling=True, prefix="basenet.slice4"
      )
      x = make_vgg_block(
          x, filters=512, n=34, pooling=False, prefix="basenet.slice4"
      )
      x = make_vgg_block(
          x, filters=512, n=37, pooling=False, prefix="basenet.slice4"
      )
      x = make_vgg_block(
          x, filters=512, n=40, pooling=True, prefix="basenet.slice4"
      )
      vgg = keras.models.Model(inputs=inputs, outputs=x)
      return [
          vgg.get_layer(slice_name).output
          for slice_name in [
              "basenet.slice1.12",
              "basenet.slice2.19",
              "basenet.slice3.29",
              "basenet.slice4.38",
          ]
      ]
  
imports: import typing
  import cv2
  import numpy
  import tensorflow
  import efficientnet.tfkeras
  from tensorflow import keras
  from .None import tools
label_desc: Use image node_id 9 for calling a global function with example usage: build_vgg_backbone(inputs) and returns: unknown