dev(narugo): add all docs

        denormalizer_name: str = _DEFAULT_DENORMALIZER_NAME,

):

    """

    Convert WD14 embedding to understandable prediction result.

    Convert WD14 embedding to understandable prediction result. This function can process both

    single embeddings (1-dimensional array) and batches of embeddings (2-dimensional array).

    :param emb: The 1-dim extracted embedding.

    :param emb: The extracted embedding(s). Can be either a 1-dim array for single image or

                2-dim array for batch processing

    :type emb: numpy.ndarray

    :param model_name: The name of the model to use.

    :param model_name: Name of the WD14 model to use for prediction

    :type model_name: str

    :param general_threshold: The threshold for general tags.

    :param general_threshold: Confidence threshold for general tags (0.0 to 1.0)

    :type general_threshold: float

    :param general_mcut_enabled: If True, applies MCut thresholding to general tags.

    :param general_mcut_enabled: Enable MCut thresholding for general tags to improve prediction quality

    :type general_mcut_enabled: bool

    :param character_threshold: The threshold for character tags.

    :param character_threshold: Confidence threshold for character tags (0.0 to 1.0)

    :type character_threshold: float

    :param character_mcut_enabled: If True, applies MCut thresholding to character tags.

    :param character_mcut_enabled: Enable MCut thresholding for character tags to improve prediction quality

    :type character_mcut_enabled: bool

    :param no_underline: If True, replaces underscores in tag names with spaces.

    :param no_underline: Replace underscores with spaces in tag names for better readability

    :type no_underline: bool

    :param drop_overlap: If True, drops overlapping tags.

    :param drop_overlap: Remove overlapping tags to reduce redundancy

    :type drop_overlap: bool

    :param fmt: Return format, default is ``('rating', 'general', 'character')``.

    :return: Prediction result based on the provided fmt.

    :param fmt: Specify return format structure for predictions, default is ``('rating', 'general', 'character')``.

    :type fmt: tuple

    :param denormalize: Whether to denormalize the embedding before prediction

    :type denormalize: bool

    :param denormalizer_name: Name of the denormalizer to use if denormalization is enabled

    :type denormalizer_name: str

    :return: For single embeddings: prediction result based on fmt. For batches: list of prediction results.

    .. note::

        Only the embeddings not get normalized can be converted to understandable prediction result.

        If normalized embeddings are provided, set ``denormalize=True`` to convert them back.

    For batch processing (2-dim input), returns a list where each element corresponds

    to one embedding's predictions in the same format as single embedding output.

    Example:

        >>> import os

        >>> import numpy as np

        >>> from imgutils.tagging import get_wd14_tags, convert_wd14_emb_to_prediction

        >>>

        >>> # extract the feature embedding

        >>> # extract the feature embedding, shape: (W, )

        >>> embedding = get_wd14_tags('skadi.jpg', fmt='embedding')

        >>>

        >>> # convert to understandable result

        >>> rating, general, character = convert_wd14_emb_to_prediction(embedding)

        >>> # these 3 dicts will be the same as that returned by `get_wd14_tags('skadi.jpg')`

        >>>

        >>> # Batch processing, shape: (B, W)

        >>> embeddings = np.stack([

        ...     get_wd14_tags('img1.jpg', fmt='embedding'),

        ...     get_wd14_tags('img2.jpg', fmt='embedding'),

        ... ])

        >>> # results will be a list of (rating, general, character) tuples

        >>> results = convert_wd14_emb_to_prediction(embeddings)

    """

    if denormalize:

        emb = denormalize_wd14_emb(