code: def _read_data(self):
      # The data, split between train and test sets:
      (x_train, y_train), (x_test, y_test) = cifar10.load_data()
  
      # Convert class vectors to binary class matrices.
      y_train = tf.keras.utils.to_categorical(y_train, num_classes)
      y_test = tf.keras.utils.to_categorical(y_test, num_classes)
  
      x_train = x_train.astype("float32")
      x_train /= 255
      x_test = x_test.astype("float32")
      x_test /= 255
  
      return (x_train, y_train), (x_test, y_test)
  
imports: from __future__ import print_function
  import argparse
  import numpy
  import tensorflow
  from tensorflow.keras.datasets import cifar10
  from tensorflow.keras.layers import Input, Dense, Dropout, Flatten
  from tensorflow.keras.layers import Convolution2D, MaxPooling2D
  from tensorflow.keras.models import Model, load_model
  from tensorflow.keras.preprocessing.image import ImageDataGenerator
  from ray import train, tune
  from ray.tune import Trainable
  from ray.tune.schedulers import PopulationBasedTraining
label_desc: Use image node_id 1 for calling the Cifar10Model obj's underlying member method code with example usage: obj._read_data() and returns: x_train,  y_train,  x_test,  y_test