def METHOD_NAME():
    instances = [{
        'bbox_label': 0,
        'bbox': [0, 0, 100, 100],
        'ignore_flag': 0,
        'instance_id': 1,
        'mot_conf': 1.0,
        'category_id': 1,
        'visibility': 1.0
    }, {
        'bbox_label': 0,
        'bbox': [0, 0, 100, 100],
        'ignore_flag': 0,
        'instance_id': 2,
        'mot_conf': 1.0,
        'category_id': 1,
        'visibility': 1.0
    }]
    instances_2 = copy.deepcopy(instances)
    sep = os.sep
    pred_instances_data = dict(
        bboxes=torch.tensor([
            [0, 0, 100, 100],
            [0, 0, 100, 40],
        ]),
        instances_id=torch.tensor([1, 2]),
        scores=torch.tensor([1.0, 1.0]))
    pred_instances_data_2 = copy.deepcopy(pred_instances_data)
    pred_instances = InstanceData(**pred_instances_data)
    pred_instances_2 = InstanceData(**pred_instances_data_2)
    img_data_sample = DetDataSample()
    img_data_sample.pred_track_instances = pred_instances
    img_data_sample.instances = instances
    img_data_sample.set_metainfo(
        dict(
            frame_id=0,
            ori_video_length=2,
            video_length=2,
            img_id=1,
            img_path=f'xxx{sep}MOT17-09-DPM{sep}img1{sep}000001.jpg',
        ))
    img_data_sample_2 = DetDataSample()
    img_data_sample_2.pred_track_instances = pred_instances_2
    img_data_sample_2.instances = instances_2
    img_data_sample_2.set_metainfo(
        dict(
            frame_id=1,
            ori_video_length=2,
            video_length=2,
            img_id=2,
            img_path=f'xxx{sep}MOT17-09-DPM{sep}img1{sep}000002.jpg',
        ))
    track_data_sample = TrackDataSample()
    track_data_sample.video_data_samples = [
        img_data_sample, img_data_sample_2
    ]
    # [TrackDataSample]
    predictions = []
    if isinstance(track_data_sample, BaseDataElement):
        predictions.append(track_data_sample.to_dict())
    return predictions