human
I wrote the below code

    def decorate(scoring_func):
        @functools.wraps(scoring_func)
        def compute_score(*args, **kwargs):
            tags_scoring_func = getcallargs(scoring_func, *args, **kwargs)
            if (('y_score' in tags_scoring_func) or ('y_prob' in tags_scoring_func) or ('y2' in tags_scoring_func)):
                raise AttributeError('The    function    {}    has    an    unsupported    attribute.    Metric    with`y_pred`    are    the    only    supported    metrics    is    the    only    supported.')
            _score = scoring_func(*args, **kwargs)
            if squared:
                _score = np.power(_score, 2)
            sens_spec_sig = signature(sensitivity_specificity_support)
            params_sens_spec = set(sens_spec_sig._parameters.keys())
            sel_params = params_sens_spec.intersection(set(tags_scoring_func))
            tags_scoring_func = dict(((k, tags_scoring_func[k]) for k in sel_params))
            if (scoring_func.__name__ == 'geometric_mean_score'):
                if ('average' in tags_scoring_func):
                    if (tags_scoring_func['average'] == 'multiclass'):
                        tags_scoring_func['average'] = 'macro'
            elif ((scoring_func.__name__ == 'accuracy_score') or (scoring_func.__name__ == 'jaccard_similarity_score')):
                tags_scoring_func['average'] = 'binary'
            tags_sens_spec = sens_spec_sig.bind(**tags_scoring_func)
            (sen, spe, _) = sensitivity_specificity_support(*tags_sens_spec.args, **tags_sens_spec.kwargs)
            dom = (sen - spe)
            return ((1.0 + (alpha * dom)) * _score)
        return compute_score
    return decorate

What's a good function header?
assistant
#Here's a good function header.

def make_index_balanced_accuracy(alpha=0.1, squared=True):