def _pad_points_and_labels(self, input_points, input_labels):
        r"""
        The method pads the 2D points and labels to the maximum number of points in the batch.
        """
        expected_nb_points = max([point.shape[0] for point in input_points])
        processed_input_points = []
        for i, point in enumerate(input_points):
            if point.shape[0] != expected_nb_points:
                point = np.concatenate(
                    [point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value], axis=0
                )
                input_labels[i] = np.append(input_labels[i], [self.point_pad_value])
            processed_input_points.append(point)
        input_points = processed_input_points
        return input_points, input_labels

        The method pads the 2D points and labels to the maximum number of points in the batch.
        
dpkd/transformers/src/transformers/models/sam/processing_sam.py