dev(narugo): add attachments

    :type no_underline: bool

    :param drop_overlap: Whether to drop overlapping tags.

    :type drop_overlap: bool

    :param fmt: The format of the output.

    :return: The post-processed results.

    :param fmt: Output format specification defining which components to include

    :type fmt: Any

    :param attachments: Additional model attachments for extended tagging capabilities

    :type attachments: Optional[Mapping[str, Union[Tuple[str, str], Tuple[str, str, dict]]]]

    :return: Processed tagging results in the specified format

    :rtype: Any

    :raises ValueError: If attachment configuration is invalid or incompatible

    """

    attachments = dict(attachments or {})

    d_attachments: Dict[str, Tuple[Attachment, dict]] = {}

    :type drop_overlap: bool

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

        ``embedding`` is also supported for feature extraction.

    :type fmt: Any

    :param attachments: Additional model attachments for extended tagging capabilities

    :type attachments: Optional[Mapping[str, Union[Tuple[str, str], Tuple[str, str, dict]]]]

    :return: Prediction result based on the provided fmt.

    :rtype: Any

    :raises ValueError: If attachment configuration is invalid or incompatible

    .. note::

        The fmt argument can include the following keys:

        This embedding is valuable for constructing indices that enable rapid querying of images based on

        visual features within large-scale datasets.

    .. note::

        The attachment system allows integration of additional tagging models to extend the base WD14 tagger's capabilities.

        Attachments are specified using a dictionary with the following format:

        .. code-block:: python

            attachments = {

                'name': ('repo_id', 'model_name'),  # Basic format

                'name': ('repo_id', 'model_name', {'threshold': 0.35})  # With additional parameters when predicting

            }

        The ``fmt`` argument can include attachment results using the format 'name/key', where:

        - ``name``: The name specified in the attachments dictionary

        - ``key``: The specific output type requested from the attachment

        For example:

        >>> from imgutils.tagging import get_wd14_tags

        >>>

        >>> # Using an attachment for additional style tagging

        >>> results = get_wd14_tags(

        ...     'image.jpg',

        ...     attachments={'monochrome': ('deepghs/eattach_monochrome_experiments', 'mlp_layer1_seed1')},

        ...     fmt=('general', 'monochrome/scores')

        ... )

        >>>

        >>> # Results will include both base tags and attachment outputs

        >>> print(results)

        (

            {'1girl': 0.99, ...},

            {'monochrome': 0.999, 'normal': 0.001},

        )

        Multiple attachments can be used simultaneously, and each attachment can provide multiple output types

        through its fmt specification. Ensure that attachment models are compatible with the base WD14 model's

        embedding format.

    Example:

        Here are some images for example

    :param drop_overlap: Remove overlapping tags to reduce redundancy

    :type drop_overlap: bool

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

    :type fmt: tuple

    :type fmt: Any

    :param attachments: Additional model attachments for extended tagging capabilities

    :type attachments: Optional[Mapping[str, Union[Tuple[str, str], Tuple[str, str, dict]]]]

    :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.

    :rtype: Any

    :raises ValueError: If attachment configuration is invalid or incompatible

    .. note::

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