code: def define_vgg_pre_processing(tf_input):
      """
      This function defines the vgg_pre_processing network using the tensorflow nn
      api.
  
      Parameters
      ----------
      tf_input: tensorflow.Tensor
          The input tensor to the network. The image is expected to be in RGB
          format.
      Returns
      -------
      out: tensorflow.Tensor
          The scaled output tensor of the network.
      """
      _tf = _lazy_import_tensorflow()
  
      scaled_input = tf_input * 255.00
      red_channel, green_channel, blue_channel = _tf.split(
          scaled_input, 3, axis=3
      )
  
      red_channel_scaled = red_channel - 123.68
      green_channel_scaled = green_channel - 116.779
      blue_channel_scaled = blue_channel - 103.939
  
      sub_scalar = _tf.concat(
          [
              red_channel_scaled,
              green_channel_scaled,
              blue_channel_scaled,
          ],
          3,
      )
  
      return sub_scalar
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from turicreate.toolkits._tf_model import TensorFlowModel
  import numpy
  import turicreate.toolkits._tf_utils
  from turicreate._deps.minimal_package import _minimal_package_import_check
label_desc: Use image node_id 7 for calling a global function with example usage: define_vgg_pre_processing(tf_input) and returns: sub_scalar