def METHOD_NAME(self, check_content=False, progress_callback=None, progress_interval=100, **kwargs):
    annotation = read_xml(self.annotation_file)
    meta = annotation.find('meta')
    size = int(meta.find('task').find('size').text)
    annotations = []
    content_errors = None if not check_content else []
    for image_id, image in enumerate(annotation.iter('image')):
        identifier = image.attrib['name'].split('/')[-1]
        if check_content:
            if not check_file_existence(self.images_dir / identifier):
                content_errors.append('{}: does not exist'.format(self.images_dir / identifier))
        label = [1]
        x_vals, y_vals = np.zeros((1, len(LABELS_TO_COCO))), np.zeros((1, len(LABELS_TO_COCO)))
        visilibity = np.zeros((1, len(LABELS_TO_COCO)))
        for point in image.iter('points'):
            point_label = point.attrib['label']
            if point_label not in LABELS_TO_COCO:
                continue
            point_id = LABELS_TO_COCO[point_label]
            point_x, point_y = point.attrib['points'].split(',')
            x_vals[0, point_id] = float(point_x)
            y_vals[0, point_id] = float(point_y)
            if int(point.attrib['occluded']):
                continue
            visilibity[0, point_id] = 2
        annotations.append(PoseEstimationAnnotation(identifier, x_vals, y_vals, visilibity, label))
        if progress_callback is not None and image_id % progress_interval == 0:
            progress_callback(image_id * 100 / size)
    return ConverterReturn(annotations, self.get_meta(), content_errors)