def METHOD_NAME(
    image: tf.Tensor,
    image_size: int = IMAGE_SIZE,
    dtype: tf.dtypes.DType = tf.float32,
    means: Tuple[float, ...] = None,
    stddev: Tuple[float, ...] = None,
) -> tf.Tensor:
    """
    Preprocesses the given image for evaluation.
    :param image: `Tensor` representing an image of arbitrary size.
    :param image_size: image height/width dimension.
    :param dtype: the dtype to convert the images to.
    :param means: values to subtract from each channel.
    :param stddev: values to divide from each channel.
    :return: a preprocessed and normalized image `Tensor`.
    """
    images = tf.image.resize_with_crop_or_pad(image, image_size, image_size)
    images = tf.cast(images, tf.float32)
    images = utils.normalize(images, means, stddev)
    images = tf.image.convert_image_dtype(images, dtype=dtype)
    return images