def METHOD_NAME(
    *, data_home=None, download_if_missing=True, return_X_y=False, as_frame=False
):
    """Load the California housing dataset (regression).
    ==============   ==============
    Samples total             20640
    Dimensionality                8
    Features                   real
    Target           real 0.15 - 5.
    ==============   ==============
    Read more in the :ref:`User Guide <california_housing_dataset>`.
    Parameters
    ----------
    data_home : str, default=None
        Specify another download and cache folder for the datasets. By default
        all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
    download_if_missing : bool, default=True
        If False, raise a IOError if the data is not locally available
        instead of trying to download the data from the source site.
    return_X_y : bool, default=False
        If True, returns ``(data.data, data.target)`` instead of a Bunch
        object.
        .. versionadded:: 0.20
    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with
        appropriate dtypes (numeric, string or categorical). The target is
        a pandas DataFrame or Series depending on the number of target_columns.
        .. versionadded:: 0.23
    Returns
    -------
    dataset : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.
        data : ndarray, shape (20640, 8)
            Each row corresponding to the 8 feature values in order.
            If ``as_frame`` is True, ``data`` is a pandas object.
        target : numpy array of shape (20640,)
            Each value corresponds to the average
            house value in units of 100,000.
            If ``as_frame`` is True, ``target`` is a pandas object.
        feature_names : list of length 8
            Array of ordered feature names used in the dataset.
        DESCR : str
            Description of the California housing dataset.
        frame : pandas DataFrame
            Only present when `as_frame=True`. DataFrame with ``data`` and
            ``target``.
            .. versionadded:: 0.23
    (data, target) : tuple if ``return_X_y`` is True
        A tuple of two ndarray. The first containing a 2D array of
        shape (n_samples, n_features) with each row representing one
        sample and each column representing the features. The second
        ndarray of shape (n_samples,) containing the target samples.
        .. versionadded:: 0.20
    Notes
    -----
    This dataset consists of 20,640 samples and 9 features.
    """
    data_home = get_data_home(data_home=data_home)
    if not exists(data_home):
        makedirs(data_home)
    filepath = _pkl_filepath(data_home, "cal_housing.pkz")
    if not exists(filepath):
        if not download_if_missing:
            raise IOError("Data not found and `download_if_missing` is False")
        logger.info(
            "Downloading Cal. housing from {} to {}".format(ARCHIVE.url, data_home)
        )
        archive_path = _fetch_remote(ARCHIVE, dirname=data_home)
        with tarfile.open(mode="r:gz", name=archive_path) as f:
            cal_housing = np.loadtxt(
                f.extractfile("CaliforniaHousing/cal_housing.data"), delimiter=","
            )
            # Columns are not in the same order compared to the previous
            # URL resource on lib.stat.cmu.edu
            columns_index = [8, 7, 2, 3, 4, 5, 6, 1, 0]
            cal_housing = cal_housing[:, columns_index]
            joblib.dump(cal_housing, filepath, compress=6)
        remove(archive_path)
    else:
        cal_housing = joblib.load(filepath)
    feature_names = [
        "MedInc",
        "HouseAge",
        "AveRooms",
        "AveBedrms",
        "Population",
        "AveOccup",
        "Latitude",
        "Longitude",
    ]
    target, data = cal_housing[:, 0], cal_housing[:, 1:]
    # avg rooms = total rooms / households
    data[:, 2] /= data[:, 5]
    # avg bed rooms = total bed rooms / households
    data[:, 3] /= data[:, 5]
    # avg occupancy = population / households
    data[:, 5] = data[:, 4] / data[:, 5]
    # target in units of 100,000
    target = target / 100000.0
    descr = load_descr("california_housing.rst")
    X = data
    y = target
    frame = None
    target_names = [
        "MedHouseVal",
    ]
    if as_frame:
        frame, X, y = _convert_data_dataframe(
            "fetch_california_housing", data, target, feature_names, target_names
        )
    if return_X_y:
        return X, y
    return Bunch(
        data=X,
        target=y,
        frame=frame,
        target_names=target_names,
        feature_names=feature_names,
        DESCR=descr,
    )