code: def get_mnist_data():
      # the data, shuffled and split between train and test sets
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
      x_train = x_train.reshape(
          x_train.shape[0], MNIST.img_rows, MNIST.img_cols, 1
      )
      x_test = x_test.reshape(
          x_test.shape[0], MNIST.img_rows, MNIST.img_cols, 1
      )
  
      x_train = x_train.astype("float32")
      x_test = x_test.astype("float32")
      x_train /= 255
      x_test /= 255
      print("x_train shape:", x_train.shape)
      print(x_train.shape[0], "train samples")
      print(x_test.shape[0], "test samples")
  
      # convert class vectors to binary class matrices
      y_train = keras.utils.to_categorical(y_train, MNIST.num_classes)
      y_test = keras.utils.to_categorical(y_test, MNIST.num_classes)
      return x_train, y_train, x_test, y_test
  
label_desc: Use image node_id 1 for calling the MNIST obj's underlying member method code with example usage: obj.get_mnist_data() and returns: x_train,  y_train,  x_test,  y_test