def METHOD_NAME():
    describe()
    print(f"Workload: {ARGS.model_name}")
    onnx_model = onnx.load(ARGS.onnx_path)
    shape_dict = {}
    for item in ARGS.input_shape:
        print(f"  input_name : {item['name']}")
        print(f"  input_shape: {item['shape']}")
        print(f"  input_dtype: {item['dtype']}")
        shape_dict[item["name"]] = item["shape"]
    mod, params = from_onnx(onnx_model, shape_dict, freeze_params=True)
    input_data = {
        item["name"]: generate_input_data(item["shape"], item["dtype"]) for item in ARGS.input_shape
    }
    with ms.Profiler() as profiler:
        database = ms.relay_integration.tune_relay(
            mod=mod,
            target=ARGS.target,
            params=params,
            work_dir=ARGS.work_dir,
            max_trials_global=ARGS.num_trials,
            num_trials_per_iter=64,
            runner=ms.runner.RPCRunner(  # type: ignore
                rpc_config=ARGS.rpc_config,
                evaluator_config=ms.runner.EvaluatorConfig(
                    number=ARGS.number,
                    repeat=ARGS.repeat,
                    min_repeat_ms=ARGS.min_repeat_ms,
                    enable_cpu_cache_flush=ARGS.cpu_flush,
                ),
                alloc_repeat=1,
            ),
            cost_model=ms.cost_model.XGBModel(  # type: ignore
                extractor=ms.feature_extractor.PerStoreFeature(),
                adaptive_training=ARGS.adaptive_training,
            ),
            strategy=ms.search_strategy.EvolutionarySearch(),
        )
        lib = ms.relay_integration.compile_relay(
            database=database,
            mod=mod,
            target=ARGS.target,
            params=params,
            backend=ARGS.backend,
        )
    print("Tuning Time:")
    print(profiler.table())
    run_module_via_rpc(
        rpc_config=ARGS.rpc_config,
        lib=lib,
        dev_type=ARGS.target.kind.name,
        args=input_data,
        continuation=create_timer(ARGS.backend),
        backend=ARGS.backend,
    )