dev(narugo): add camie pydocs

        - ``micro_opt``: Micro-optimized thresholds

        - ``macro_opt``: Macro-optimized thresholds

    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 numpy as np

        >>> from imgutils.tagging import get_camie_tags, convert_camie_emb_to_prediction

        >>>

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

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

        >>>

        >>> # convert to understandable result

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

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

        >>>

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

        >>> embeddings = np.stack([

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

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

        ... ])

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

        >>> results = convert_camie_emb_to_prediction(embeddings)

    """

    model = _get_camie_emb_to_pred_model(model_name=model_name, is_refined=is_refined)

    if len(emb.shape) == 1:

    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

        >>>