def generate(self, x: np.ndarray, y: np.ndarray | None = None, **kwargs) -> np.ndarray:
        """
        Generate adversarial samples and return them in an array.

        :param x: An array with the original inputs to be attacked.
        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
                  (nb_samples,).
        :return: An array holding the adversarial examples.
        """
        if y is not None:
            y = check_and_transform_label_format(y, nb_classes=self.estimator.nb_classes)

        # Check that `y` is provided for targeted attacks
        if self.targeted and y is None:  # pragma: no cover
            raise ValueError("Target labels `y` need to be provided for a targeted attack.")

        # No labels provided, use model prediction as correct class
        if y is None:
            y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))

        if self.estimator.nb_classes == 2 and y.shape[1] == 1:  # pragma: no cover
            raise ValueError(
                "This attack has not yet been tested for binary classification with a single output classifier."
            )

        # Compute adversarial examples with implicit batching
        nb_batches = int(np.ceil(x.shape[0] / float(self.batch_size)))
        x_adv_list = []
        for batch_id in trange(nb_batches, desc="ZOO", disable=not self.verbose):
            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
            x_batch = x[batch_index_1:batch_index_2]
            y_batch = y[batch_index_1:batch_index_2]
            res = self._generate_batch(x_batch, y_batch)
            x_adv_list.append(res)
        x_adv = np.vstack(x_adv_list)

        # Apply clip
        if self.estimator.clip_values is not None:
            clip_min, clip_max = self.estimator.clip_values
            np.clip(x_adv, clip_min, clip_max, out=x_adv)

        # Log success rate of the ZOO attack
        logger.info(
            "Success rate of ZOO attack: %.2f%%",
            100 * compute_success(self.estimator, x, y, x_adv, self.targeted, batch_size=self.batch_size),
        )

        return x_adv

        Generate adversarial samples and return them in an array.

        :param x: An array with the original inputs to be attacked.
        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
                  (nb_samples,).
        :return: An array holding the adversarial examples.
        
art/attacks/evasion/zoo.py