def METHOD_NAME():
    boxes = torch.tensor(
        [[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.3],
          [-10.0, 23.0, 16.0, 10, 20, 20, 0.5]]],
        dtype=torch.float32).cuda(
        )  # boxes (m, 7) with bottom center in lidar coordinate
    pts = torch.tensor(
        [[[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6],
          [0.8, 1.2, 3.9], [-9.2, 21.0, 18.2], [3.8, 7.9, 6.3],
          [4.7, 3.5, -12.2], [3.8, 7.6, -2], [-10.6, -12.9, -20], [
              -16, -18, 9
          ], [-21.3, -52, -5], [0, 0, 0], [6, 7, 8], [-2, -3, -4]]],
        dtype=torch.float32).cuda()  # points (n, 3) in lidar coordinate
    point_indices = points_in_boxes_all(points=pts, boxes=boxes)
    expected_point_indices = torch.tensor(
        [[[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [0, 1], [0, 0], [0, 0],
          [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]],
        dtype=torch.int32).cuda()
    assert point_indices.shape == torch.Size([1, 15, 2])
    assert (point_indices == expected_point_indices).all()
    if torch.cuda.device_count() > 1:
        pts = pts.to('cuda:1')
        boxes = boxes.to('cuda:1')
        expected_point_indices = expected_point_indices.to('cuda:1')
        point_indices = points_in_boxes_all(points=pts, boxes=boxes)
        assert point_indices.shape == torch.Size([1, 15, 2])
        assert (point_indices == expected_point_indices).all()