def METHOD_NAME():
    # Load the data and split it between train and test sets
    (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
    # Scale images to the [0, 1] range
    x_train = x_train.astype("float32") / 255
    x_test = x_test.astype("float32") / 255
    # Make sure images have shape (28, 28, 1)
    x_train = np.expand_dims(x_train, -1)
    x_test = np.expand_dims(x_test, -1)
    # convert class vectors to binary class matrices
    y_train = keras.utils.to_categorical(y_train, 10)
    y_test = keras.utils.to_categorical(y_test, 10)
    return x_train, y_train, x_test, y_test