};

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

	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()

	const isWriteLock = false
	return lm.lockLoop(ctx, id, source, timeout, isWriteLock)
}

func (lm *LRWMutex) lock(id, source string, isWriteLock bool) (locked bool) {
	lm.mu.Lock()
	defer lm.mu.Unlock()

	lm.id = id
	lm.source = source
	if isWriteLock {
		if lm.ref == 0 && !lm.isWriteLock {
			lm.ref = 1
			lm.isWriteLock = true
			locked = true
		}
	} else {
		if !lm.isWriteLock {

		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

					}
					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{

  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()) {

}

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
}

			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.

			if p.ActiveWorkers() < maxWorkers {
				p.mu.RLock()
				workers := min(len(p.workers)+1, maxWorkers)
				existing := len(p.workers)
				p.mu.RUnlock()
				p.ResizeWorkers(workers, existing)
			}
			maxMRFWorkers := min(maxWorkers, MRFWorkerMaxLimit)
			if p.ActiveMRFWorkers() < maxMRFWorkers {
				p.mu.RLock()
				workers := min(p.mrfWorkerSize+1, maxMRFWorkers)
				p.mu.RUnlock()
				p.ResizeFailedWorkers(workers)

	return workers[h%uint64(len(workers))]
}

func (es *expiryState) ResizeWorkers(n int) {
	if n == 0 {
		n = 100
	}

	// Lock to avoid multiple resizes to happen at the same time.
	es.mu.Lock()
	defer es.mu.Unlock()
	var workers []chan expiryOp
	if v := es.workers.Load(); v != nil {
		// Copy to new array.
		workers = append(workers, *v...)
	}

	if n == len(workers) || n < 1 {
		return
	}