def METHOD_NAME(self, data_batch: Sequence[dict],
            data_samples: 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 (dict): A batch of data from the dataloader.
        data_samples (Sequence[dict]): A batch of data samples that
            contain annotations and predictions.
    """
    for data_sample in data_samples:
        pred = data_sample['pred_instances']
        # `bboxes` is sorted by `scores`
        ranked_scores, rank_inds = pred['scores'].sort(descending=True)
        ranked_bboxes = pred['bboxes'][rank_inds, :]
        ranked_bboxes = ranked_bboxes.cpu().numpy()
        ranked_scores = ranked_scores.cpu().numpy()
        pred_instance = InstanceData()
        pred_instance.bboxes = ranked_bboxes
        pred_instance.scores = ranked_scores
        if self.num_max_proposals is not None:
            pred_instance = pred_instance[:self.num_max_proposals]
        img_path = data_sample['img_path']
        # `file_name` is the key to obtain the proposals from the
        # `proposals_list`.
        file_name = osp.join(
            osp.split(osp.split(img_path)[0])[-1],
            osp.split(img_path)[-1])
        result = {file_name: pred_instance}
        self.results.append(result)