def METHOD_NAME():
    """Normalize_yuv"""
    data_shape = (1, 3, 96, 96)
    init_net = caffe2_pb2.NetDef()
    init_net.name = "test_init_net"
    init_net.external_output[:] = ["A", "mean", "std"]
    init_net.op.extend(
        [
            core.CreateOperator(
                "GivenTensorFill",
                [],
                ["A"],
                shape=data_shape,
                values=np.random.uniform(size=data_shape).flatten().tolist(),
            ),
            core.CreateOperator(
                "GivenTensorFill",
                [],
                ["mean"],
                shape=(
                    1,
                    3,
                ),
                values=np.random.uniform(size=3).flatten().tolist(),
            ),
            core.CreateOperator(
                "GivenTensorFill",
                [],
                ["std"],
                shape=(
                    1,
                    3,
                ),
                values=np.random.uniform(size=3).flatten().tolist(),
            ),
        ]
    )
    predict_net = caffe2_pb2.NetDef()
    predict_net.name = "test_predict_net"
    predict_net.external_input[:] = ["A", "mean", "std"]
    predict_net.external_output[:] = ["C"]
    predict_net.op.extend(
        [
            core.CreateOperator(
                "NormalizePlanarYUV",
                ["A", "mean", "std"],
                ["C"],
            )
        ]
    )
    model = Model(init_net, predict_net)
    verify_caffe2_forward_impl(model, data_shape, data_shape)