def METHOD_NAME(mean_list, stdev_list):
    """
    Normalises returns so they have the in sample vol
    >>> vol_equaliser([1.,2.],[2.,4.])
    ([1.5, 1.5], [3.0, 3.0])
    >>> vol_equaliser([1.,2.],[np.nan, np.nan])
    ([nan, nan], [nan, nan])
    """
    if np.all(np.isnan(stdev_list)):
        return ([np.nan] * len(mean_list), [np.nan] * len(stdev_list))
    avg_stdev = np.nanmean(stdev_list)
    norm_factor = [asset_stdev / avg_stdev for asset_stdev in stdev_list]
    with np.errstate(invalid="ignore"):
        norm_means = [
            mean_list[i] / norm_factor[i] for (i, notUsed) in enumerate(mean_list)
        ]
        norm_stdev = [
            stdev_list[i] / norm_factor[i] for (i, notUsed) in enumerate(stdev_list)
        ]
    return (norm_means, norm_stdev)