def check_backdoor_effective(self, backdoor_data: np.ndarray, backdoor_labels: np.ndarray) -> bool:
        """
        Check if supposed backdoors are effective against the classifier

        :param backdoor_data: data with the backdoor added
        :param backdoor_labels: the correct label for the data
        :return: true if any of the backdoors are effective on the model
        """
        backdoor_predictions = self._predict_classifier(backdoor_data)
        backdoor_effective = np.logical_not(np.all(backdoor_predictions == backdoor_labels, axis=1))
        return np.any(backdoor_effective)  # type: ignore

        Check if supposed backdoors are effective against the classifier

        :param backdoor_data: data with the backdoor added
        :param backdoor_labels: the correct label for the data
        :return: true if any of the backdoors are effective on the model
        
art/estimators/poison_mitigation/neural_cleanse/neural_cleanse.py