How to use the quantulum3.language.topdir function in quantulum3

"n_jobs": n_jobs,
            "alpha": 0.0001,
            "fit_intercept": True,
            "random_state": 0,
        }

    _LOGGER.info("Fit SGD Classifier")
    clf = SGDClassifier(**parameters).fit(matrix, train_target)
    obj = {
        "scikit-learn_version": pkg_resources.get_distribution("scikit-learn").version,
        "tfidf_model": tfidf_model,
        "clf": clf,
        "target_names": target_names,
    }
    if store:  # pragma: no cover
        path = language.topdir(lang).joinpath("clf.joblib")
        _LOGGER.info("Store classifier at {}".format(path))
        with path.open("wb") as file:
            joblib.dump(obj, file)
    return obj

def training_set(lang="en_US"):
    training_set_ = []

    path = language.topdir(lang).joinpath("train")
    for file in path.iterdir():
        if file.suffix == ".json":
            with file.open("r", encoding="utf-8") as train_file:
                training_set_ += json.load(train_file)

    return training_set_

def __init__(self, obj=None, lang="en_US"):
        """
        Load the intent classifier
        """
        self.tfidf_model = None
        self.classifier = None
        self.target_names = None

        if not USE_CLF:
            return

        if not obj:
            path = language.topdir(lang).joinpath("clf.joblib")
            with path.open("rb") as file:
                obj = joblib.load(file)

        cur_scipy_version = pkg_resources.get_distribution("scikit-learn").version
        if cur_scipy_version != obj.get("scikit-learn_version"):  # pragma: no cover
            _LOGGER.warning(
                "The classifier was built using a different scikit-learn "
                "version (={}, !={}). The disambiguation tool could behave "
                "unexpectedly. Consider running classifier.train_classfier()".format(
                    obj.get("scikit-learn_version"), cur_scipy_version
                )
            )

        self.tfidf_model = obj["tfidf_model"]
        self.classifier = obj["clf"]
        self.target_names = obj["target_names"]

if isinstance(unit, classes.Unit):
            surfaces.update(unit.surfaces)
            surfaces.update(unit.symbols)
        for surface in surfaces:
            neighbours = v.most_similar(
                v.query(surface), topn=topn, min_similarity=min_similarity
            )
            training_set.append(
                {
                    "unit": name,
                    "text": " ".join(neighbour[0] for neighbour in neighbours),
                }
            )
    print("Done")

    with language.topdir(lang).joinpath("train/similars.json").open(
        "w", encoding="utf-8"
    ) as file:
        json.dump(training_set, file, sort_keys=True, indent=4)

assert len(set(names)) == len(general_entities)
        except AssertionError:  # pragma: no cover
            raise Exception(
                "Entities with same name: %s" % [i for i in names if names.count(i) > 1]
            )

        self.names = dict(
            (
                k["name"],
                c.Entity(name=k["name"], dimensions=k["dimensions"], uri=k["URI"]),
            )
            for k in general_entities
        )

        # Update with language specific URI
        with TOPDIR.joinpath(language.topdir(lang), "entities.json").open(
            "r", encoding="utf-8"
        ) as file:
            lang_entities = json.load(file)
        for ent in lang_entities:
            general_entities[ent["name"]].uri = ent["URI"]

        # Generate derived units
        derived_ent = defaultdict(set)
        for entity in self.names.values():
            if not entity.dimensions:
                continue
            perms = self.get_dimension_permutations(entity.dimensions)
            for perm in perms:
                key = get_key_from_dimensions(perm)
                derived_ent[key].add(entity)

objs = []
    for num, page in enumerate(pages):

        obj = {
            "_id": page[1],
            "url": "https://{}.wikipedia.org/wiki/{}".format(lang[:2], page[1]),
            "clean": page[1].replace("_", " "),
        }

        print("---> Downloading %s (%d of %d)" % (obj["clean"], num + 1, len(pages)))

        obj["text"] = wikipedia.page(obj["clean"], auto_suggest=False).content
        obj["unit"] = page[0]
        objs.append(obj)

    path = language.topdir(lang).joinpath("train/wiki.json")
    if store:
        with path.open("w") as wiki_file:
            json.dump(objs, wiki_file, indent=4, sort_keys=True)

    print("\n---> All done.\n")
    return objs