def _adversarial_satisfactory(
        self, samples: np.ndarray, target: int, clip_min: float, clip_max: float
    ) -> np.ndarray:
        """
        Check whether an image is adversarial.

        :param samples: A batch of examples.
        :param target: The target label.
        :param clip_min: Minimum value of an example.
        :param clip_max: Maximum value of an example.
        :return: An array of 0/1.
        """
        samples = np.clip(samples, clip_min, clip_max)
        preds = np.argmax(self.estimator.predict(samples, batch_size=self.batch_size), axis=1)

        if self.targeted:
            result = preds == target
        else:
            result = preds != target

        return result

        Check whether an image is adversarial.

        :param samples: A batch of examples.
        :param target: The target label.
        :param clip_min: Minimum value of an example.
        :param clip_max: Maximum value of an example.
        :return: An array of 0/1.
        
art/attacks/evasion/hop_skip_jump.py