def certify(preds: np.ndarray, labels: np.ndarray) -> np.ndarray:
        """
        Check if the data has been certifiably classified correct.

        :param preds: interval predictions
        :param labels: associated labels (not one-hot encoded).
        :return: array of True or False if predictions are certifiable
        """

        cert_bounds = np.copy(preds[:, 1])  # Take the upper bounds of all the predictions
        for i, label in enumerate(labels):
            cert_bounds[i, label] = preds[i, 0, label]  # Replace the correct prediction with its lower bound
        return np.argmax(cert_bounds, axis=1) == labels

        Check if the data has been certifiably classified correct.

        :param preds: interval predictions
        :param labels: associated labels (not one-hot encoded).
        :return: array of True or False if predictions are certifiable
        
art/estimators/certification/interval/interval.py