def METHOD_NAME(
        self, data_samples: List[DetDataSample]) -> DetDataSample:
    """Merge predictions which come form the different views of one image
    to one prediction.
    Args:
        data_samples (List[DetDataSample]): List of predictions
        of enhanced data which come form one image.
    Returns:
        List[DetDataSample]: Merged prediction.
    """
    aug_bboxes = []
    aug_scores = []
    aug_labels = []
    img_metas = []
    # TODO: support instance segmentation TTA
    assert data_samples[0].pred_instances.get('masks', None) is None, \
        'TTA of instance segmentation does not support now.'
    for data_sample in data_samples:
        aug_bboxes.append(data_sample.pred_instances.bboxes)
        aug_scores.append(data_sample.pred_instances.scores)
        aug_labels.append(data_sample.pred_instances.labels)
        img_metas.append(data_sample.metainfo)
    merged_bboxes, merged_scores = self.merge_aug_bboxes(
        aug_bboxes, aug_scores, img_metas)
    merged_labels = torch.cat(aug_labels, dim=0)
    if merged_bboxes.numel() == 0:
        return data_samples[0]
    det_bboxes, keep_idxs = batched_nms(merged_bboxes, merged_scores,
                                        merged_labels, self.tta_cfg.nms)
    det_bboxes = det_bboxes[:self.tta_cfg.max_per_img]
    det_labels = merged_labels[keep_idxs][:self.tta_cfg.max_per_img]
    results = InstanceData()
    _det_bboxes = det_bboxes.clone()
    results.bboxes = _det_bboxes[:, :-1]
    results.scores = _det_bboxes[:, -1]
    results.labels = det_labels
    det_results = data_samples[0]
    det_results.pred_instances = results
    return det_results