How to use the pingouin._flatten_list function in pingouin

def anovan(data=None, dv=None, between=None, ss_type=2, export_filename=None):
    """N-way ANOVA using statsmodels.

    This is an internal function. The main call to this function should be done
    by the :py:func:`pingouin.anova` function.
    """
    # Check that stasmodels is installed
    from pingouin.utils import _is_statsmodels_installed
    _is_statsmodels_installed(raise_error=True)
    from statsmodels.api import stats
    from statsmodels.formula.api import ols

    # Validate the dataframe
    _check_dataframe(dv=dv, between=between, data=data, effects='between')
    all_cols = _flatten_list([dv, between])
    bad_chars = [',', '(', ')', ':']
    if not all([c not in v for c in bad_chars for v in all_cols]):
        err_msg = "comma, bracket, and colon are not allowed in column names."
        raise ValueError(err_msg)

    # Drop missing values
    data = data[all_cols].dropna()
    assert data.shape[0] >= 5, 'Data must have at least 5 non-missing values.'

    # Reset index (avoid duplicate axis error)
    data = data.reset_index(drop=True)

    # Create R-like formula
    formula = dv + ' ~ '
    for fac in between:
        formula += 'C(' + fac + ', Sum) * '

>>> ancova(data=df, dv='Scores', covar=['Income', 'BMI'], between='Method')
         Source        SS  DF       F     p-unc
    0    Method   552.284   3   3.233  0.036113
    1    Income  1573.952   1  27.637  0.000011
    2       BMI    60.014   1   1.054  0.312842
    3  Residual  1708.509  30     NaN       NaN
    """
    # Safety checks
    assert isinstance(data, pd.DataFrame)
    assert dv in data.columns, '%s is not in data.' % dv
    assert between in data.columns, '%s is not in data.' % between
    assert isinstance(covar, (str, list)), 'covar must be a str or a list.'

    # Drop missing values
    data = data[_flatten_list([dv, between, covar])].dropna()

    # Check the number of covariates
    if isinstance(covar, list):
        if len(covar) > 1:
            return ancovan(dv=dv, covar=covar, between=between, data=data,
                           export_filename=export_filename)
        else:
            covar = covar[0]

    # Assert that covariate is numeric
    assert data[covar].dtype.kind in 'fi', 'Covariate must be numeric.'

    def linreg(x, y):
        return np.corrcoef(x, y)[0, 1] * np.std(y, ddof=1) / np.std(x, ddof=1)

    # Compute slopes