code: def masking_noise(data, sess, v):
      
      data_noise = data.copy()
      rand = tf.random_uniform(data.shape)
      data_noise[sess.run(tf.nn.relu(tf.sign(v - rand))).astype(np.bool)] = 0
  
      return data_noise
docs: Apply masking noise to data in X.
  
      In other words a fraction v of elements of X
      (chosen at random) is forced to zero.
      :param data: array_like, Input data
      :param sess: TensorFlow session
      :param v: fraction of elements to distort, float
      :return: transformed data