}
		emitted[info] = true

		infoLhs := info.lhs // possibly nil (see declInfo.lhs field comment)
		if infoLhs == nil {
			infoLhs = []*Var{v}
		}
		init := &Initializer{infoLhs, info.init}
		check.Info.InitOrder = append(check.Info.InitOrder, init)
	}

	if debug {
		fmt.Println()
		fmt.Println("Initialization order:")
		for _, init := range check.Info.InitOrder {

Yan Zhulanow <******@****.***> 1692687376 +0900

//
//    // archAvailableIEEE reports whether an architecture-specific CRC32-IEEE
//    // algorithm is available.
//    archAvailableIEEE() bool
//
//    // archInitIEEE initializes the architecture-specific CRC3-IEEE algorithm.
//    // It can only be called if archAvailableIEEE() returns true.
//    archInitIEEE()
//
//    // archUpdateIEEE updates the given CRC32-IEEE. It can only be called if

	"internal/abi"
	"unsafe"
)

type mOS struct {
	initialized bool
	mutex       pthreadmutex
	cond        pthreadcond
	count       int
}

func unimplemented(name string) {
	println(name, "not implemented")
	*(*int)(unsafe.Pointer(uintptr(1231))) = 1231
}

//go:nosplit
func semacreate(mp *m) {
	if mp.initialized {
		return
	}
	mp.initialized = true
	if err := pthread_mutex_init(&mp.mutex, nil); err != 0 {

            if (!initialized) {
                DisposableUtil.add(BeanDescFactory::clear);
                initialized = true;
            }
        }
    }

    /**
     * キャッシュをクリアします。
     */
    public static void clear() {
        beanDescCache.clear();
        initialized = false;
    }

  "tf_saved_model.asset"() {filename = "assets/table.txt", sym_name = "v"} : () -> ()
  "tf_saved_model.session_initializer"() {initializers = [@NoOp_0, @NoOp_1]} : () -> ()
// Check that the initializer typed "init_op" is removed from initializers list.
// CHECK: "tf_saved_model.session_initializer"()
// CHECK-SAME: initializers = []

func.func @NoOp_0(%arg0: tensor<!tf_type.string> {tf_saved_model.bound_input = @v})

  let summary = "Initializes TensorFlow session state.";
  let description = [{
    The session initializer op marks one or more functions that must be called
    by an external agent exactly once to initialize TensorFlow session state,
    and this must happen before any other exported functions are called. There
    must be no more than one session initializer op in a saved model.

module attributes {tf_saved_model.semantics} {
  // Test case: No session initializer op
}

// -----

module attributes {tf_saved_model.semantics} {
  // Test case: No matching function for the given session initializer.
  // expected-error@+1 {{'tf_saved_model.session_initializer' op the initializer function does not exist}}

    static NativeServices nativeServices;
    static boolean initialized;

    public static synchronized void initialize() {
        if (!initialized) {
            System.setProperty("org.gradle.native", "true");
            File nativeDir = getNativeServicesDir();
            NativeServices.initializeOnDaemon(nativeDir, NativeServicesMode.fromSystemProperties());
            initialized = true;
        }
    }

// NewFromFunc allocates a new cached value instance and initializes it with an
// update function, making it ready for use.
func NewFromFunc[T any](ttl time.Duration, opts Opts, update func(ctx context.Context) (T, error)) *Cache[T] {
	return &Cache[T]{
		ttl:      ttl,
		updateFn: update,
		opts:     opts,
	}
}

// InitOnce initializes the cache with a TTL and an update function. It is
// guaranteed to be called only once.