def get_gt_fut_trajs(nusc: NuScenes,
                     anno,
                     cs_record,
                     pose_record,
                     fut_ts) -> None:
    """
    Visualizes a sample from BEV with annotations and detection results.
    :param nusc: NuScenes object.
    """
    box = Box(anno['translation'], anno['size'], Quaternion(anno['rotation']))
    # Move box to ego vehicle coord system.
    box.translate(-np.array(pose_record['translation']))
    box.rotate(Quaternion(pose_record['rotation']).inverse)
    #  Move box to sensor coord system.
    box.translate(-np.array(cs_record['translation']))
    box.rotate(Quaternion(cs_record['rotation']).inverse)
    
    # get future trajectory coords for each box
    gt_fut_trajs = np.zeros((fut_ts, 2))  # [fut_ts*2]
    gt_fut_masks = np.zeros((fut_ts))  # [fut_ts]
    gt_fut_trajs[:] = box.center[:2]
    cur_box = box
    cur_anno = anno
    for i in range(fut_ts):
        if cur_anno['next'] != '':
            anno_next = nusc.get('sample_annotation', cur_anno['next'])
            box_next = Box(
                anno_next['translation'], anno_next['size'], Quaternion(anno_next['rotation'])
            )
            # Move box to ego vehicle coord system.
            box_next.translate(-np.array(pose_record['translation']))
            box_next.rotate(Quaternion(pose_record['rotation']).inverse)
            #  Move box to sensor coord system.
            box_next.translate(-np.array(cs_record['translation']))
            box_next.rotate(Quaternion(cs_record['rotation']).inverse)
            # gt_fut_trajs[i] = box_next.center[:2]
            gt_fut_trajs[i] = box_next.center[:2] - cur_box.center[:2]
            gt_fut_masks[i] = 1
            cur_anno = anno_next
            cur_box = box_next
        else:
            # gt_fut_trajs[i:] = gt_fut_trajs[i-1]
            gt_fut_trajs[i:] = 0
            break         

    return gt_fut_trajs.reshape(-1).tolist(), gt_fut_masks.reshape(-1).tolist()

    Visualizes a sample from BEV with annotations and detection results.
    :param nusc: NuScenes object.
    
tools/analysis_tools/visualization.py