def brier_score(self, scores: Union[List[float], List[List]]) -> float:
        """
        assess both calibration and sharpness of the probability estimates
        Args:
            scores (List[float]): relevance scores returned by feedback function
        Returns:
            float: Brier score
        """
        if isinstance(scores[0], List):
            scores = [score for score, _ in scores]
        assert len(scores) == len(self.true_labels)
        brier_score = 0

        for score, truth in zip(scores, self.true_labels):
            brier_score += (score - truth) ** 2

        return brier_score / len(scores)

        assess both calibration and sharpness of the probability estimates
        Args:
            scores (List[float]): relevance scores returned by feedback function
        Returns:
            float: Brier score
        
src/feedback/trulens/feedback/groundtruth.py