How to use the quantulum3.load.entities function in quantulum3

def disambiguate_entity(key, text, lang="en_US"):
    """
    Resolve ambiguity between entities with same dimensionality.
    """
    try:
        if clf.USE_CLF:
            ent = clf.disambiguate_entity(key, text, lang)
        else:
            derived = load.entities().derived[key]
            if len(derived) > 1:
                ent = no_clf.disambiguate_no_classifier(derived, text, lang)
                ent = load.entities().names[ent]
            elif len(derived) == 1:
                ent = next(iter(derived))
            else:
                ent = None
    except (KeyError, StopIteration):
        ent = None

    return ent

def disambiguate_entity(key, text, lang="en_US"):
    """
    Resolve ambiguity between entities with same dimensionality.
    """

    new_ent = next(iter(load.entities(lang).derived[key]))
    if len(load.entities(lang).derived[key]) > 1:
        transformed = classifier(lang).tfidf_model.transform([clean_text(text, lang)])
        scores = classifier(lang).classifier.predict_proba(transformed).tolist()[0]
        scores = zip(scores, classifier(lang).target_names)

        # Filter for possible names
        names = [i.name for i in load.entities(lang).derived[key]]
        scores = [i for i in scores if i[1] in names]

        # Sort by rank
        scores = sorted(scores, key=lambda x: x[0], reverse=True)
        try:
            new_ent = load.entities(lang).names[scores[0][1]]
        except IndexError:
            _LOGGER.debug('\tAmbiguity not resolved for "%s"', str(key))

def ambiguous_units(lang="en_US"):  # pragma: no cover
    """
    Determine ambiguous units
    :return: list ( tuple( key, list (Unit) ) )
    """
    ambiguous = [
        i for i in list(load.units(lang).surfaces_all.items()) if len(i[1]) > 1
    ]
    ambiguous += [i for i in list(load.units(lang).symbols.items()) if len(i[1]) > 1]
    ambiguous += [i for i in list(load.entities(lang).derived.items()) if len(i[1]) > 1]
    return ambiguous

"""

    new_ent = next(iter(load.entities(lang).derived[key]))
    if len(load.entities(lang).derived[key]) > 1:
        transformed = classifier(lang).tfidf_model.transform([clean_text(text, lang)])
        scores = classifier(lang).classifier.predict_proba(transformed).tolist()[0]
        scores = zip(scores, classifier(lang).target_names)

        # Filter for possible names
        names = [i.name for i in load.entities(lang).derived[key]]
        scores = [i for i in scores if i[1] in names]

        # Sort by rank
        scores = sorted(scores, key=lambda x: x[0], reverse=True)
        try:
            new_ent = load.entities(lang).names[scores[0][1]]
        except IndexError:
            _LOGGER.debug('\tAmbiguity not resolved for "%s"', str(key))

    return new_ent

def disambiguate_entity(key, text, lang="en_US"):
    """
    Resolve ambiguity between entities with same dimensionality.
    """
    try:
        if clf.USE_CLF:
            ent = clf.disambiguate_entity(key, text, lang)
        else:
            derived = load.entities().derived[key]
            if len(derived) > 1:
                ent = no_clf.disambiguate_no_classifier(derived, text, lang)
                ent = load.entities().names[ent]
            elif len(derived) == 1:
                ent = next(iter(derived))
            else:
                ent = None
    except (KeyError, StopIteration):
        ent = None

    return ent