def get_2d_boxes(nusc,
                 sample_data_token: str,
                 visibilities: List[str],
                 mono3d=True):
    """Get the 2D annotation records for a given `sample_data_token`.

    Args:
        sample_data_token (str): Sample data token belonging to a camera \
            keyframe.
        visibilities (list[str]): Visibility filter.
        mono3d (bool): Whether to get boxes with mono3d annotation.

    Return:
        list[dict]: List of 2D annotation record that belongs to the input
            `sample_data_token`.
    """

    # Get the sample data and the sample corresponding to that sample data.
    sd_rec = nusc.get('sample_data', sample_data_token)

    assert sd_rec[
        'sensor_modality'] == 'camera', 'Error: get_2d_boxes only works' \
        ' for camera sample_data!'
    if not sd_rec['is_key_frame']:
        raise ValueError(
            'The 2D re-projections are available only for keyframes.')

    s_rec = nusc.get('sample', sd_rec['sample_token'])

    # Get the calibrated sensor and ego pose
    # record to get the transformation matrices.
    cs_rec = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token'])
    pose_rec = nusc.get('ego_pose', sd_rec['ego_pose_token'])
    camera_intrinsic = np.array(cs_rec['camera_intrinsic'])

    # Get all the annotation with the specified visibilties.
    ann_recs = [
        nusc.get('sample_annotation', token) for token in s_rec['anns']
    ]
    ann_recs = [
        ann_rec for ann_rec in ann_recs
        if (ann_rec['visibility_token'] in visibilities)
    ]

    repro_recs = []

    for ann_rec in ann_recs:
        # Augment sample_annotation with token information.
        ann_rec['sample_annotation_token'] = ann_rec['token']
        ann_rec['sample_data_token'] = sample_data_token

        # Get the box in global coordinates.
        box = nusc.get_box(ann_rec['token'])

        # Move them to the ego-pose frame.
        box.translate(-np.array(pose_rec['translation']))
        box.rotate(Quaternion(pose_rec['rotation']).inverse)

        # Move them to the calibrated sensor frame.
        box.translate(-np.array(cs_rec['translation']))
        box.rotate(Quaternion(cs_rec['rotation']).inverse)

        # Filter out the corners that are not in front of the calibrated
        # sensor.
        corners_3d = box.corners()
        in_front = np.argwhere(corners_3d[2, :] > 0).flatten()
        corners_3d = corners_3d[:, in_front]

        # Project 3d box to 2d.
        corner_coords = view_points(corners_3d, camera_intrinsic,
                                    True).T[:, :2].tolist()

        # Keep only corners that fall within the image.
        final_coords = post_process_coords(corner_coords)

        # Skip if the convex hull of the re-projected corners
        # does not intersect the image canvas.
        if final_coords is None:
            continue
        else:
            min_x, min_y, max_x, max_y = final_coords

        # Generate dictionary record to be included in the .json file.
        repro_rec = generate_record(ann_rec, min_x, min_y, max_x, max_y,
                                    sample_data_token, sd_rec['filename'])

        # If mono3d=True, add 3D annotations in camera coordinates
        if mono3d and (repro_rec is not None):
            loc = box.center.tolist()

            dim = box.wlh
            dim[[0, 1, 2]] = dim[[1, 2, 0]]  # convert wlh to our lhw
            dim = dim.tolist()

            rot = box.orientation.yaw_pitch_roll[0]
            rot = [-rot]  # convert the rot to our cam coordinate

            global_velo2d = nusc.box_velocity(box.token)[:2]
            global_velo3d = np.array([*global_velo2d, 0.0])
            e2g_r_mat = Quaternion(pose_rec['rotation']).rotation_matrix
            c2e_r_mat = Quaternion(cs_rec['rotation']).rotation_matrix
            cam_velo3d = global_velo3d @ np.linalg.inv(
                e2g_r_mat).T @ np.linalg.inv(c2e_r_mat).T
            velo = cam_velo3d[0::2].tolist()

            repro_rec['bbox_cam3d'] = loc + dim + rot
            repro_rec['velo_cam3d'] = velo

            center3d = np.array(loc).reshape([1, 3])
            center2d = points_cam2img(
                center3d, camera_intrinsic, with_depth=True)
            repro_rec['center2d'] = center2d.squeeze().tolist()
            # normalized center2D + depth
            # if samples with depth < 0 will be removed
            if repro_rec['center2d'][2] <= 0:
                continue

            ann_token = nusc.get('sample_annotation',
                                 box.token)['attribute_tokens']
            if len(ann_token) == 0:
                attr_name = 'None'
            else:
                attr_name = nusc.get('attribute', ann_token[0])['name']
            attr_id = nus_attributes.index(attr_name)
            repro_rec['attribute_name'] = attr_name
            repro_rec['attribute_id'] = attr_id

        repro_recs.append(repro_rec)

    return repro_recs
Get the 2D annotation records for a given `sample_data_token`.

    Args:
        sample_data_token (str): Sample data token belonging to a camera             keyframe.
        visibilities (list[str]): Visibility filter.
        mono3d (bool): Whether to get boxes with mono3d annotation.

    Return:
        list[dict]: List of 2D annotation record that belongs to the input
            `sample_data_token`.
    
tools/data_converter/vad_nuscenes_converter.py