code: def featurewise_std_normalization(X, std=None):
      """featurewise_std_normalization.
  
      Scale each sample by the specified standard deviation. If no std
      specified, std is evaluated over all samples data.
  
      Arguments:
          X: `array`. The batch of samples to scale.
          std: `float`. The std to use for scaling data. If not specified, it
              will be evaluated over the provided data.
  
      Returns:
          A numpy array with same shape as input. Or a tuple (array, std) if no
          std value was specified.
  
      """
      if std is None:
          std = np.std(X, axis=0)
          return X / std, std
      else:
          return X / std
  
imports: from __future__ import division, print_function, absolute_import
  import os
  import random
  import numpy
  from PIL import Image
  import pickle
  import csv
  import warnings
  import tensorflow.compat.v1
  from io import BytesIO
label_desc: Use image node_id 23 for calling a global function with example usage: featurewise_std_normalization(X, std) and returns: unknown,  std, unknown