def load_gt(nusc: NuScenes, eval_split: str, box_cls, verbose: bool = False):
    """
    Loads ground truth boxes from DB.
    :param nusc: A NuScenes instance.
    :param eval_split: The evaluation split for which we load GT boxes.
    :param box_cls: Type of box to load, e.g. DetectionBox or TrackingBox.
    :param verbose: Whether to print messages to stdout.
    :return: The GT boxes.
    """

    # Init.
    if box_cls == DetectionBox_modified:
        attribute_map = {a['token']: a['name'] for a in nusc.attribute}

    if verbose:
        print('Loading annotations for {} split from nuScenes version: {}'.format(eval_split, nusc.version))
    # Read out all sample_tokens in DB.
    sample_tokens_all = [s['token'] for s in nusc.sample]
    assert len(sample_tokens_all) > 0, "Error: Database has no samples!"

    # Only keep samples from this split.
    splits = create_splits_scenes()

    # Check compatibility of split with nusc_version.
    version = nusc.version
    if eval_split in {'train', 'val', 'train_detect', 'train_track'}:
        assert version.endswith('trainval'), \
            'Error: Requested split {} which is not compatible with NuScenes version {}'.format(eval_split, version)
    elif eval_split in {'mini_train', 'mini_val'}:
        assert version.endswith('mini'), \
            'Error: Requested split {} which is not compatible with NuScenes version {}'.format(eval_split, version)
    elif eval_split == 'test':
        assert version.endswith('test'), \
            'Error: Requested split {} which is not compatible with NuScenes version {}'.format(eval_split, version)
    else:
        raise ValueError('Error: Requested split {} which this function cannot map to the correct NuScenes version.'
                         .format(eval_split))

    if eval_split == 'test':
        # Check that you aren't trying to cheat :).
        assert len(nusc.sample_annotation) > 0, \
            'Error: You are trying to evaluate on the test set but you do not have the annotations!'
    index_map = {}
    for scene in nusc.scene:
        first_sample_token = scene['first_sample_token']
        sample = nusc.get('sample', first_sample_token)
        index_map[first_sample_token] = 1
        index = 2
        while sample['next'] != '':
            sample = nusc.get('sample', sample['next'])
            index_map[sample['token']] = index
            index += 1

    sample_tokens = []
    for sample_token in sample_tokens_all:
        scene_token = nusc.get('sample', sample_token)['scene_token']
        scene_record = nusc.get('scene', scene_token)
        if scene_record['name'] in splits[eval_split]:
            sample_tokens.append(sample_token)

    all_annotations = EvalBoxes()

    # Load annotations and filter predictions and annotations.
    tracking_id_set = set()
    for sample_token in tqdm.tqdm(sample_tokens, leave=verbose):

        sample = nusc.get('sample', sample_token)
        sample_annotation_tokens = sample['anns']

        sample_boxes = []
        for sample_annotation_token in sample_annotation_tokens:

            sample_annotation = nusc.get('sample_annotation', sample_annotation_token)
            if box_cls == DetectionBox_modified:
                # Get label name in detection task and filter unused labels.
                detection_name = category_to_detection_name(sample_annotation['category_name'])
                if detection_name is None:
                    continue

                # Get attribute_name.
                attr_tokens = sample_annotation['attribute_tokens']
                attr_count = len(attr_tokens)
                if attr_count == 0:
                    attribute_name = ''
                elif attr_count == 1:
                    attribute_name = attribute_map[attr_tokens[0]]
                else:
                    raise Exception('Error: GT annotations must not have more than one attribute!')

                sample_boxes.append(
                    box_cls(
                        token=sample_annotation_token,
                        sample_token=sample_token,
                        translation=sample_annotation['translation'],
                        size=sample_annotation['size'],
                        rotation=sample_annotation['rotation'],
                        velocity=nusc.box_velocity(sample_annotation['token'])[:2],
                        num_pts=sample_annotation['num_lidar_pts'] + sample_annotation['num_radar_pts'],
                        detection_name=detection_name,
                        detection_score=-1.0,  # GT samples do not have a score.
                        attribute_name=attribute_name,
                        visibility=sample_annotation['visibility_token'],
                        index=index_map[sample_token]
                    )
                )
            elif box_cls == TrackingBox:
                assert False
            else:
                raise NotImplementedError('Error: Invalid box_cls %s!' % box_cls)

        all_annotations.add_boxes(sample_token, sample_boxes)

    if verbose:
        print("Loaded ground truth annotations for {} samples.".format(len(all_annotations.sample_tokens)))

    return all_annotations

    Loads ground truth boxes from DB.
    :param nusc: A NuScenes instance.
    :param eval_split: The evaluation split for which we load GT boxes.
    :param box_cls: Type of box to load, e.g. DetectionBox or TrackingBox.
    :param verbose: Whether to print messages to stdout.
    :return: The GT boxes.
    
projects/mmdet3d_plugin/datasets/nuscenes_eval.py