code: def convert_net(img_example):
      """
      convert image array to original
      Args:
           img_example: array data of img
      """
      # reshape img_example
      output_img = np.reshape(
          img_example.astype("float32"), (3, 224, 224)
      )
  
      output_img *= img_std
      output_img += img_mean
      output_img *= 255
      output_img = np.reshape(
          output_img.astype(np.uint8), (3, 224, 224)
      )
  
      # convert C,H,W to H,W,C
      output_img = output_img.transpose((1, 2, 0))
  
      return output_img
  
file_path: AdvBox/example/utility.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import distutils.util
  import numpy
  from PIL import Image
  import os
  from paddle.fluid import core
label_desc: Use image node_id 4 for calling a global function with example usage: convert_net(img_example) and returns: output_img