def METHOD_NAME(test_case, device):
    input = flow.tensor(
        np.ones(shape=(3, 1, 1), dtype=np.float32),
        dtype=flow.float32,
        device=flow.device(device),
        requires_grad=True,
    )
    like_tensor = flow.tensor(
        np.ones(shape=(3, 3, 3), dtype=np.float32),
        dtype=flow.float32,
        device=flow.device(device),
        requires_grad=True,
    )
    of_out = flow.broadcast_like(input, like_tensor, broadcast_axes=(1, 2))
    of_out = of_out.sum()
    of_out.backward()
    np_grad = [[[9.0]], [[9.0]], [[9.0]]]
    test_case.assertTrue(np.allclose(input.grad.numpy(), np_grad, 1e-05, 1e-05))