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)
    target_label = self.select_label(meta).find('name').text
    meta = self.get_meta()
    gender_classes_mapping = meta['gender_label_map']
    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))
        for bbox in image:
            if 'label' not in bbox.attrib.keys() or bbox.attrib['label'] != target_label:
                continue
            annotation_dict = {}
            bbox_rect = [
                float(bbox.attrib['xtl']), float(bbox.attrib['ytl']),
                float(bbox.attrib['xbr']), float(bbox.attrib['ybr'])
            ]
            for attribute in bbox.iter('attribute'):
                attribute_name = attribute.attrib['name']
                if attribute_name == 'gender':
                    attribute_label = gender_classes_mapping[attribute.text]
                    attribute_annotation = ClassificationAnnotation(identifier, attribute_label)
                    attribute_annotation.metadata['rect'] = bbox_rect
                    annotation_dict['gender_annotation'] = attribute_annotation
                if attribute_name == 'age':
                    attribute_value = int(attribute.text)
                    attribute_label = self.get_age_class(attribute_value)
                    attribute_class_annotation = ClassificationAnnotation(identifier, attribute_label)
                    attribute_regression_annotation = RegressionAnnotation(identifier, attribute_value)
                    attribute_class_annotation.metadata['rect'] = bbox_rect
                    attribute_regression_annotation.metadata['rect'] = bbox_rect
                    annotation_dict['age_class_annotation'] = attribute_class_annotation
                    annotation_dict['age_regression_annotation'] = attribute_regression_annotation
            annotations.append(ContainerAnnotation(annotation_dict))
        if progress_callback is not None and image_id % progress_interval == 0:
            progress_callback(image_id * 100 / size)
    return ConverterReturn(annotations, meta, content_errors)