def METHOD_NAME(self,
            inputs: Tensor,
            data_samples: TrackSampleList,
            rescale: bool = True) -> TrackSampleList:
    """Predict results from a batch of inputs and data samples with
    postprocessing.
    Args:
        inputs (Tensor): of shape (N, T, C, H, W) encoding
            input images. The N denotes batch size.
            The T denotes the number of frames in a video.
        data_samples (list[:obj:`TrackDataSample`]): The batch
            data samples. It usually includes information such
            as `video_data_samples`.
        rescale (bool, Optional): If False, then returned bboxes and masks
            will fit the scale of img, otherwise, returned bboxes and masks
            will fit the scale of original image shape. Defaults to True.
    Returns:
        TrackSampleList: Tracking results of the inputs.
    """
    assert inputs.dim() == 5, 'The img must be 5D Tensor (N, T, C, H, W).'
    assert len(data_samples) == 1, \
        'Mask2former only support 1 batch size per gpu for now.'
    # [T, C, H, W]
    img = inputs[0]
    track_data_sample = data_samples[0]
    feats = self.backbone(img)
    pred_track_ins_list = self.track_head.METHOD_NAME(feats, track_data_sample,
                                                  rescale)
    det_data_samples_list = []
    for idx, pred_track_ins in enumerate(pred_track_ins_list):
        img_data_sample = track_data_sample[idx]
        img_data_sample.pred_track_instances = pred_track_ins
        det_data_samples_list.append(img_data_sample)
    results = TrackDataSample()
    results.video_data_samples = det_data_samples_list
    return [results]