// Return a shape op fetching the shape of `a`.
TFE_Op* ShapeOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return an allreduce op adding up input tensor `in` from `group_size` workers.
TFE_Op* AllReduceOp(TFE_Context* ctx, TFE_TensorHandle* in, int group_size);

// Return a SendOp op `op_name` with send input tensor `in` and attributes
// `send_device`, `recv_device`, and `send_device_incarnation` set.

  "durations" : [ {
    "testProject" : "nativeMonolithic",
    "linux" : 1380
  }, {
    "testProject" : "nativeMonolithicOverlapping",
    "linux" : 1275
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.nativeplatform.RealWorldNativePluginPerformanceTest.build with 12 parallel workers",
  "durations" : [ {
    "testProject" : "nativeMonolithic",

HSPLkotlinx/coroutines/scheduling/CoroutineScheduler$Worker;->nextInt(I)I
HSPLkotlinx/coroutines/scheduling/CoroutineScheduler$Worker;->pollGlobalQueues()Lkotlinx/coroutines/scheduling/Task;
HSPLkotlinx/coroutines/scheduling/CoroutineScheduler$Worker;->run()V
HSPLkotlinx/coroutines/scheduling/CoroutineScheduler$Worker;->setIndexInArray(I)V
HSPLkotlinx/coroutines/scheduling/CoroutineScheduler$Worker;->setNextParkedWorker(Ljava/lang/Object;)V

func (opts Config) Clone() (int, time.Duration, string) {
	configMutex.RLock()
	defer configMutex.RUnlock()
	return opts.IOCount, opts.Sleep, opts.Bitrot
}

// GetWorkers returns the number of workers, -1 is none configured
func (opts Config) GetWorkers() int {
	configMutex.RLock()
	defer configMutex.RUnlock()
	return opts.DriveWorkers
}

// Update updates opts with nopts

///

Event loop'u bloke etmemek için, *path operation function*'ı `async def` yerine normal `def` ile tanımlayabilirsiniz; böylece FastAPI ana döngüyü bloke etmemek için bunu bir thread pool worker (iş parçacığı havuzu çalışanı) üzerinde çalıştırır.

{* ../../docs_src/stream_data/tutorial002_py310.py ln[30:34] hl[31] *}

/// tip | İpucu

  /**
   * Returns the {@link Executor} that will be used to run this service. Subclasses may override
   * this method to use a custom {@link Executor}, which may configure its worker thread with a
   * specific name, thread group or priority. The returned executor's {@link
   * Executor#execute(Runnable) execute()} method is called when this service is started and
   * stopped, and should return promptly.

  protected void triggerShutdown() {}

  /**
   * Returns the {@link Executor} that will be used to run this service. Subclasses may override
   * this method to use a custom {@link Executor}, which may configure its worker thread with a
   * specific name, thread group or priority. The returned executor's {@link
   * Executor#execute(Runnable) execute()} method is called when this service is started, and should
   * return promptly.
   *

          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "title": "Avg. Active Workers",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "fieldConfig": {
        "defaults": {

| `minio_node_replication_average_active_workers`         | Average number of active replication workers                                                             |
| `minio_node_replication_max_active_workers`             | Maximum number of active replication workers seen since server start                                     |

load, number of objects in the object store as well as storage speed. In addition, any bandwidth limits set via `mc admin bucket remote add` could also contribute to replication speed. The number of workers used for replication defaults to 100. Based on network bandwidth and system load, the number of workers used in replication can be configured using `mc admin config set alias api` to set the `replication_workers`. The prometheus metrics exposed by MinIO can be used to plan resource allocation and bandwidth...