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)

        # Initialize variables
        dims = list(x.shape[1:])
        self._nb_features = np.prod(dims)
        x_adv = np.reshape(x.astype(ART_NUMPY_DTYPE), (-1, self._nb_features))
        preds = np.argmax(self.estimator.predict(x, batch_size=self.batch_size), axis=1)

        # Determine target classes for attack
        if y is None:
            # Randomly choose target from the incorrect classes for each sample
            from art.utils import random_targets

            targets = np.argmax(random_targets(preds, self.estimator.nb_classes), axis=1)
        else:
            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."
                )

            targets = np.argmax(y, axis=1)

        # Compute perturbation with implicit batching
        for batch_id in trange(
            int(np.ceil(x_adv.shape[0] / float(self.batch_size))), desc="JSMA", disable=not self.verbose
        ):
            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
            batch = x_adv[batch_index_1:batch_index_2]

            # Main algorithm for each batch
            # Initialize the search space; optimize to remove features that can't be changed
            if self.estimator.clip_values is not None:
                search_space = np.zeros(batch.shape)
                clip_min, clip_max = self.estimator.clip_values
                if self.theta > 0:
                    search_space[batch < clip_max] = 1
                else:  # pragma: no cover
                    search_space[batch > clip_min] = 1
            else:
                search_space = np.ones(batch.shape)

            # Get current predictions
            current_pred = preds[batch_index_1:batch_index_2]
            target = targets[batch_index_1:batch_index_2]
            active_indices = np.where(current_pred != target)[0]
            all_feat = np.zeros_like(batch)

            while active_indices.size != 0:
                # Compute saliency map
                feat_ind = self._saliency_map(
                    np.reshape(batch, [batch.shape[0]] + dims)[active_indices],
                    target[active_indices],
                    search_space[active_indices],
                )

                # Update used features
                all_feat[active_indices, feat_ind[:, 0]] = 1
                all_feat[active_indices, feat_ind[:, 1]] = 1

                # Apply attack with clipping
                if self.estimator.clip_values is not None:
                    # Prepare update depending on theta
                    if self.theta > 0:
                        clip_func, clip_value = np.minimum, clip_max  # type: ignore
                    else:  # pragma: no cover
                        clip_func, clip_value = np.maximum, clip_min  # type: ignore

                    # Update adversarial examples
                    tmp_batch = batch[active_indices]
                    tmp_batch[np.arange(len(active_indices)), feat_ind[:, 0]] = clip_func(
                        clip_value,
                        tmp_batch[np.arange(len(active_indices)), feat_ind[:, 0]] + self.theta,
                    )
                    tmp_batch[np.arange(len(active_indices)), feat_ind[:, 1]] = clip_func(
                        clip_value,
                        tmp_batch[np.arange(len(active_indices)), feat_ind[:, 1]] + self.theta,
                    )
                    batch[active_indices] = tmp_batch

                    # Remove indices from search space if max/min values were reached
                    search_space[batch == clip_value] = 0

                # Apply attack without clipping
                else:
                    tmp_batch = batch[active_indices]
                    tmp_batch[np.arange(len(active_indices)), feat_ind[:, 0]] += self.theta
                    tmp_batch[np.arange(len(active_indices)), feat_ind[:, 1]] += self.theta
                    batch[active_indices] = tmp_batch

                # Recompute model prediction
                current_pred = np.argmax(
                    self.estimator.predict(np.reshape(batch, [batch.shape[0]] + dims)),
                    axis=1,
                )

                # Update active_indices
                active_indices = np.where(
                    (current_pred != target)
                    * (np.sum(all_feat, axis=1) / self._nb_features <= self.gamma)
                    * (np.sum(search_space, axis=1) > 0)
                )[0]

            x_adv[batch_index_1:batch_index_2] = batch

        x_adv = np.reshape(x_adv, x.shape)

        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/saliency_map.py