code: def VGG(feature, im_height=224, im_width=224, num_classes=1000):
      # tensorflow中的tensor通道排序是NHWC
      input_image = layers.Input(
          shape=(im_height, im_width, 3), dtype="float32"
      )
      x = feature(input_image)
      x = layers.Flatten()(x)
      x = layers.Dropout(rate=0.5)(x)
      x = layers.Dense(
          2048,
          activation="relu",
          kernel_initializer=DENSE_KERNEL_INITIALIZER,
      )(x)
      x = layers.Dropout(rate=0.5)(x)
      x = layers.Dense(
          2048,
          activation="relu",
          kernel_initializer=DENSE_KERNEL_INITIALIZER,
      )(x)
      x = layers.Dense(
          num_classes, kernel_initializer=DENSE_KERNEL_INITIALIZER
      )(x)
      output = layers.Softmax()(x)
      model = Model(inputs=input_image, outputs=output)
      return model
  
label_desc: Use image node_id 1 for calling a global function with example usage: VGG(feature, im_height, im_width, num_classes) and returns: model