def METHOD_NAME(img_rows=28,
                      img_cols=28,
                      num_classes=10):
  """Get data for mnist training and evaluation."""
  (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
  if tf.keras.backend.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
  else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
  # 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)
  return x_train, y_train, x_test, y_test