def METHOD_NAME(model_str, output_path):
    if output_path.exists():
        del_dir(output_path)
    output_path.mkdir()
    output_path_str = os.fspath(output_path)
    print(model_str)
    print("getting model...")
    net, params = get_model(model_str)
    try:
        os.mkdir(model_str)
    except FileExistsError:
        pass
    print("building...")
    with tvm.transform.PassContext(opt_level=3):
        graph, lib, params = relay.build(net, tvm.target.Target(target, target_host), params=params)
    print("dumping lib...")
    lib.export_library(output_path_str + "/" + "deploy_lib_cpu.so", fcompile=ndk.create_shared)
    print("dumping graph...")
    with open(output_path_str + "/" + "deploy_graph.json", "w") as f:
        f.write(graph)
    print("dumping params...")
    with open(output_path_str + "/" + "deploy_param.params", "wb") as f:
        f.write(tvm.runtime.save_param_dict(params))
    print("dumping labels...")
    synset_url = "".join(
        [
            "https://gist.githubusercontent.com/zhreshold/",
            "4d0b62f3d01426887599d4f7ede23ee5/raw/",
            "596b27d23537e5a1b5751d2b0481ef172f58b539/",
            "imagenet1000_clsid_to_human.txt",
        ]
    )
    synset_path = output_path_str + "/image_net_labels"
    download(synset_url, output_path_str + "/image_net_labels")
    with open(synset_path) as fi:
        synset = eval(fi.read())
        with open(output_path_str + "/image_net_labels.json", "w") as fo:
            json.dump(synset, fo, indent=4)
    os.remove(synset_path)