def METHOD_NAME(self, data_batch: Sequence[Tuple[Any, dict]],
            predictions: Sequence[dict]) -> None:
    """Process one batch of data samples and predictions. The processed
    results should be stored in ``self.results``, which will be used to
    compute the metrics when all batches have been processed.
    Args:
        data_batch (Sequence[Tuple[Any, dict]]): A batch of data
            from the dataloader.
        predictions (Sequence[dict]): A batch of outputs from
            the model.
    """
    for pred in predictions:
        self.results.append(pred)
    if self.metric_type == 'AR@AN':
        data_batch = data_batch['data_samples']
        for data_sample in data_batch:
            video_info = data_sample.metainfo
            video_id = video_info['video_name'][2:]
            this_video_gt = []
            for ann in video_info['annotations']:
                t_start, t_end = ann['segment']
                label = ann['label']
                this_video_gt.append([t_start, t_end, label])
            self.ground_truth[video_id] = np.array(this_video_gt)