final Object lock = new Object();
        final Thread t = Thread.currentThread();

        this.executor.submit(new Runnable() {

            @Override
            public void run () {

                try {
                    synchronized ( lock ) {
                        lock.wait(1000);
                    }

                    Thread.sleep(100);

	return pathJoin(bucketMetaPrefix, bucket, metacachePrefix, id)
}

// objectPath returns the object path of the cache.
func (o *listPathOptions) objectPath(block int) string {
	return pathJoin(metacachePrefixForID(o.Bucket, o.ID), "block-"+strconv.Itoa(block)+".s2")
}

func (o *listPathOptions) SetFilter() {
	switch {
	case metacacheSharePrefix:
		return
	case o.Prefix == o.BaseDir:
		// No additional prefix

 *   <li>It is easy for the user to ensure that listeners are never invoked while holding locks.
 * </ul>
 *
 * The last point is subtle. Often the observable object will be managing its own internal state
 * using a lock, however it is dangerous to dispatch listeners while holding a lock because they
 * might run on the {@code directExecutor()} or be otherwise re-entrant (call back into your

	exit_1
fi

enabled_minio1=$(./mc stat --json minio1/newbucket-olock | jq -r .ObjectLock.enabled)
if [ $? -ne 0 ]; then
	echo "expected bucket to be mirrored with object-lock but not present, exiting..."
	exit_1
fi

if [ "${enabled_minio1}" != "Enabled" ]; then
	echo "expected bucket to be mirrored with object-lock enabled, exiting..."
	exit_1
fi

set +x

// if it is not successful yet.
func (l *Init) Do(f func() error) error {
	if atomic.LoadUint32(&l.done) == 0 {
		return l.do(f)
	}
	return nil
}

func (l *Init) do(f func() error) error {
	l.m.Lock()
	defer l.m.Unlock()
	if atomic.LoadUint32(&l.done) == 0 {
		if err := f(); err != nil {
			return err
		}
		// Mark as done only when f() is successful
		atomic.StoreUint32(&l.done, 1)
	}
	return nil
}

/tensorflow/lite/**/coreml/**/BUILD
/tensorflow/lite/**/ios/BUILD
/tensorflow/lite/**/objc/BUILD
/tensorflow/lite/**/swift/BUILD
/tensorflow/lite/examples/ios/simple/data/*.tflite
/tensorflow/lite/examples/ios/simple/data/*.txt
Podfile.lock
Pods

.idea
.ipynb_checkpoints
.mypy_cache
.vscode
__pycache__
.pytest_cache
htmlcov
dist
site
.coverage*
coverage.xml
.netlify
test.db
log.txt
Pipfile.lock
env3.*
env
docs_build
site_build
venv
docs.zip
archive.zip

# vim temporary files
*~
.*.sw?
.cache

# macOS

	// https://issuetracker.google.com/issues/73230216.
	lockPath := filepath.Join(os.TempDir(), "go_android_exec-adb-lock")
	lock, err := os.OpenFile(lockPath, os.O_CREATE|os.O_RDWR, 0666)
	if err != nil {
		return 0, err
	}
	defer lock.Close()
	if err := syscall.Flock(int(lock.Fd()), syscall.LOCK_EX); err != nil {
		return 0, err
	}

	// In case we're booting a device or emulator alongside all.bash, wait for

	// This ensures that Close will block until Read has completed.
	// This allows another goroutine to close the reader.
	pr.closedMu.Lock()
	defer pr.closedMu.Unlock()
	if pr.closed {
		return 0, errors.New("progressReader: read after Close")
	}
	return pr.processedReader.Read(p)
}

func (pr *progressReader) Close() error {
	pr.closedMu.Lock()
	defer pr.closedMu.Unlock()
	if pr.closed {
		return nil

   * of reads may not be immediately reflected on the algorithm's data structures. These structures
   * are guarded by a lock and operations are applied in batches to avoid lock contention. The
   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.