def METHOD_NAME(self,
            inputs: Tensor,
            data_samples: TrackSampleList,
            rescale: bool = True,
            **kwargs) -> TrackSampleList:
    """Test without augmentation.
    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, \
        'MaskTrackRCNN only support 1 batch size per gpu for now.'
    track_data_sample = data_samples[0]
    video_len = len(track_data_sample)
    if track_data_sample[0].frame_id == 0:
        self.tracker.reset()
    for frame_id in range(video_len):
        img_data_sample = track_data_sample[frame_id]
        single_img = inputs[:, frame_id].contiguous()
        x = self.detector.extract_feat(single_img)
        rpn_results_list = self.detector.rpn_head.METHOD_NAME(
            x, [img_data_sample])
        # det_results List[InstanceData]
        det_results = self.detector.roi_head.METHOD_NAME(
            x, rpn_results_list, [img_data_sample], rescale=rescale)
        assert len(det_results) == 1, 'Batch inference is not supported.'
        assert 'masks' in det_results[0], 'There are no mask results.'
        img_data_sample.pred_instances = det_results[0]
        frame_pred_track_instances = self.tracker.track(
            model=self, feats=x, data_sample=img_data_sample, **kwargs)
        img_data_sample.pred_track_instances = frame_pred_track_instances
    return [track_data_sample]