return
	}

	ri.mu.Lock()
	defer ri.mu.Unlock()

	if success {
		ri.Bucket = bucket
		ri.Object = info.Name
	}
	ri.countItem(info.Size, info.DeleteMarker, success, attempt)
}

func (ri *batchJobInfo) trackCurrentBucketBatch(bucket string, batch []ObjectInfo) {
	if ri == nil {
		return
	}

	ri.mu.Lock()
	defer ri.mu.Unlock()

	ri.Bucket = bucket

itibaren
itibariyle
kadar
karşın
katrilyon
kendi
kendilerine
kendini
kendisi
kendisine
kendisini
kez
ki
kim
kimden
kime
kimi
kimse
kırk
milyar
milyon
mu
mü
mı
nasıl
ne
neden
nedenle
nerde
nerede
nereye
niye
niçin
o
olan
olarak
oldu
olduğu
olduğunu
olduklarını
olmadı
olmadığı
olmak
olması

void TF_DeleteGraph(TF_Graph* g) {
  if (g == nullptr) return;
  g->mu.lock();
  g->delete_requested = true;
  const bool del = g->sessions.empty();
  g->mu.unlock();
  if (del) delete g;
}

TF_Operation* TF_GraphOperationByName(TF_Graph* graph, const char* oper_name) {
  mutex_lock l(graph->mu);
  auto iter = graph->name_map.find(oper_name);
  if (iter == graph->name_map.end()) {

		ExpirationWorkers: 100,
		TransitionWorkers: 100,
	},
}

type ilmConfig struct {
	mu  sync.RWMutex
	cfg ilm.Config
}

func (c *ilmConfig) getExpirationWorkers() int {
	c.mu.RLock()
	defer c.mu.RUnlock()

	return c.cfg.ExpirationWorkers
}

func (c *ilmConfig) getTransitionWorkers() int {
	c.mu.RLock()
	defer c.mu.RUnlock()

	return c.cfg.TransitionWorkers
}

};

struct TF_Graph {
  TF_Graph();

  mutable tensorflow::mutex mu;
  tensorflow::Graph graph TF_GUARDED_BY(mu);

  // Runs shape inference.
  tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu);

  // Maps from name of an operation to the Node* in 'graph'.
  std::unordered_map<tensorflow::string, tensorflow::Node*> name_map
      TF_GUARDED_BY(mu);

  // The keys of this map are all the active sessions using this graph. Each

  return nullptr;
#else
  TF_Library* lib_handle = new TF_Library;
  static tensorflow::mutex mu(tensorflow::LINKER_INITIALIZED);
  static std::unordered_map<std::string, void*>* loaded_libs =
      new std::unordered_map<std::string, void*>();
  tensorflow::Env* env = tensorflow::Env::Default();
  {
    tensorflow::mutex_lock lock(mu);
    auto it = loaded_libs->find(library_filename);
    if (it != loaded_libs->end()) {

	var incorrectConfigs []error
	var retries int
	var mu sync.Mutex
	for onlineServers < len(clnts)/2 {
		var wg sync.WaitGroup
		wg.Add(len(clnts))
		onlineServers = 0
		for _, clnt := range clnts {
			go func(clnt *bootstrapRESTClient) {
				defer wg.Done()

				if clnt.gridConn.State() != grid.StateConnected {
					mu.Lock()

}

func (d *naughtyDisk) Close() (err error) {
	if err = d.calcError(); err != nil {
		return err
	}
	return d.disk.Close()
}

func (d *naughtyDisk) calcError() (err error) {
	d.mu.Lock()
	defer d.mu.Unlock()
	d.callNR++
	if err, ok := d.errors[d.callNR]; ok {
		return err
	}
	if d.defaultErr != nil {
		return d.defaultErr
	}
	return nil
}

					}
					uploadsCancel()
					return
				}
				response := time.Since(t)
				atomic.AddUint64(&totalBytesWritten, uint64(info.Size))
				objCountPerThread[i]++
				mu.Lock()
				uploadTimes = append(uploadTimes, response)
				mu.Unlock()
			}
		}(i)
	}
	wg.Wait()

	// We already saw write failures, no need to proceed into read's
	if retError != "" {
		return SpeedTestResult{

			go func(i int, set *erasureObjects) {
				defer wg.Done()
				err := set.listPath(listCtx, o, innerResults)
				mu.Lock()
				defer mu.Unlock()
				if err == nil {
					allAtEOF = false
				}
				errs[i] = err
			}(len(errs), set)
			errs = append(errs, nil)
		}
	}
	mu.Unlock()

	// Gather results to a single channel.
	// Quorum is one since we are merging across sets.