dev(narugo): doc update for hand

"""

Overview:

    Detect human hands in anime images.

    Trained on dataset `deepghs/anime_hand_detection <https://huggingface.co/datasets/deepghs/anime_hand_detection>`_ with YOLOv8.

    This module provides functionality for detecting human hands in anime images.

    .. image:: hand_detect_demo.plot.py.svg

        :align: center

    It uses YOLOv8 models trained on the

    `deepghs/anime_hand_detection <https://huggingface.co/datasets/deepghs/anime_hand_detection>`_ dataset

    from HuggingFace. The module offers a main function :func:`detect_hands` for hand detection in anime images,

    with options to choose different model levels and versions for balancing speed and accuracy.

    This is an overall benchmark of all the hand detect models:

    .. image:: hand_detect_benchmark.plot.py.svg

                 conf_threshold: float = 0.35, iou_threshold: float = 0.7) \

        -> List[Tuple[Tuple[int, int, int, int], str, float]]:

    """

    Overview:

    Detect human hand points in anime images.

    :param image: Image to detect.

    :param level: The model level being used can be either `s` or `n`.

        The `n` model runs faster with smaller system overhead, while the `s` model achieves higher accuracy.

        The default value is `s`.

    :param version: Version of model, default is ``v1.0``.

    :param max_infer_size: The maximum image size used for model inference, if the image size exceeds this limit,

        the image will be resized and used for inference. The default value is `640` pixels.

    :param conf_threshold: The confidence threshold, only detection results with confidence scores above

        this threshold will be returned. The default value is `0.3`.

    :param iou_threshold: The detection area coverage overlap threshold, areas with overlaps above this threshold

        will be discarded. The default value is `0.7`.

    :return: The detection results list, each item includes the detected area `(x0, y0, x1, y1)`,

        the target type (always `hand`) and the target confidence score.

    This function uses a YOLOv8 model to detect hands in the given anime image. It allows for

    configuration of the model level, version, and detection thresholds to suit different use cases.

    :param image: Image to detect. Can be various types as defined by ImageTyping.

    :type image: ImageTyping

    :param level: The model level being used, either 's' or 'n'.

                  's' (standard) offers higher accuracy, while 'n' (nano) provides faster processing.

    :type level: str

    :param version: Version of the model to use. Default is 'v1.0'.

    :type version: str

    :param model_name: Optional custom model name. If not provided, it's constructed from version and level.

    :type model_name: Optional[str]

    :param conf_threshold: Confidence threshold for detections. Only detections with confidence

                           above this value are returned. Default is 0.35.

    :type conf_threshold: float

    :param iou_threshold: Intersection over Union (IOU) threshold for non-maximum suppression.

                          Detections with IOU above this value are considered overlapping and merged.

                          Default is 0.7.

    :type iou_threshold: float

    :return: A list of detection results. Each result is a tuple containing:

             - Bounding box coordinates as (x0, y0, x1, y1)

             - Class label (always 'hand' for this function)

             - Confidence score

    :rtype: List[Tuple[Tuple[int, int, int, int], str, float]]

    :raises: May raise exceptions related to image loading or model inference.

    Example:

        >>> from PIL import Image

        >>> image = Image.open('anime_image.jpg')

        >>> results = detect_hands(image, level='s', conf_threshold=0.4)

        >>> for bbox, label, conf in results:

        ...     print(f"Hand detected at {bbox} with confidence {conf}")

    """

    return yolo_predict(

        image=image,