code: def normalize(array, min_value=0.0, max_value=1.0):
      """Normalizes the numpy array to (min_value, max_value)
  
      Args:
          array: The numpy array
          min_value: The min value in normalized array (Default value = 0)
          max_value: The max value in normalized array (Default value = 1)
  
      Returns:
          The array normalized to range between (min_value, max_value)
      """
      arr_min = np.min(array)
      arr_max = np.max(array)
      normalized = (array - arr_min) / (arr_max - arr_min + K.epsilon())
      return (max_value - min_value) * normalized + min_value
  
imports: from __future__ import absolute_import
  from __future__ import division
  import os
  import tempfile
  import math
  import json
  import six
  import numpy
  import matplotlib.font_manager
  from skimage import io, transform
  from keras import backend
  from keras.models import load_model
  import logging
label_desc: Use image node_id 17 for calling a global function with example usage: normalize(array, min_value, max_value) and returns: unknown