def METHOD_NAME():
    def softmax(x):
        # clip to shiftx, otherwise, when calc loss with
        # log(exp(shiftx)), may get log(0)=INF
        shiftx = (x - np.max(x)).clip(-64.)
        exps = np.exp(shiftx)
        return exps / np.sum(exps)
    M = 1000
    C = 80
    BOX_SIZE = 4
    scores_lod = np.random.random((M, C)).astype('float32')
    scores_lod = np.apply_along_axis(softmax, 1, scores_lod)
    boxes_lod = np.random.random((M, C, BOX_SIZE)).astype('float32')
    boxes_lod[:, :, 0:2] = boxes_lod[:, :, 0:2] * 10
    boxes_lod[:, :, 2:4] = boxes_lod[:, :, 2:4] * 10 + 10
    return boxes_lod, scores_lod