researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)

* Kullandığın geliştirme araçları ile iyi çalışır **<abbr title="Integrated Development Environment, kod editörüne benzer">IDE</abbr>/<abbr title="Code errorlarınızı inceleyen program">linter</abbr>/brain**:
    * Pydantic'in veri yapıları aslında sadece senin tanımladığın classlar; Bu yüzden doğrulanmış dataların ile otomatik tamamlama, linting ve mypy'ı kullanarak sorunsuz bir şekilde çalışabilirsin

	// for this instance of unlocker, we should let the
	// caller know that lock is not successfully released
	// yet.
	if len(locks)-tolerance == tolerance {
		// In case of split brain scenarios where
		// tolerance is exactly half of the len(*locks)
		// then we need to make sure we have unlocked
		// upto tolerance+1 - especially for RUnlock
		// to ensure that we don't end up with active

# Tests

tf_cuda_library(
    name = "c_test_util",
    testonly = 1,
    srcs = ["c_test_util.cc"],
    hdrs = ["c_test_util.h"],
    visibility = [
        "//learning/brain:__subpackages__",
        "//tensorflow:__subpackages__",
    ],
    deps = [
        ":c_api",
        ":c_api_experimental",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",

    ],
)

tf_cuda_library(
    name = "c_api_test_util",
    testonly = 1,
    srcs = ["c_api_test_util.cc"],
    hdrs = ["c_api_test_util.h"],
    visibility = [
        "//learning/brain:__subpackages__",
        "//tensorflow:__subpackages__",
    ],
    deps = [
        ":c_api",
        ":c_api_experimental",
        ":c_api_internal",
        "//tensorflow/c:c_test_util",

  }

  private static <T> int drain(
      BlockingQueue<T> q,
      Collection<? super T> buffer,
      int maxElements,
      long timeout,
      TimeUnit unit,
      boolean interruptibly)
      throws InterruptedException {
    return interruptibly
        ? Queues.drain(q, buffer, maxElements, timeout, unit)

  }

  private static <T> int drain(
      BlockingQueue<T> q,
      Collection<? super T> buffer,
      int maxElements,
      long timeout,
      TimeUnit unit,
      boolean interruptibly)
      throws InterruptedException {
    return interruptibly
        ? Queues.drain(q, buffer, maxElements, timeout, unit)

			select {
			case currentGlobalBuffer <- v:
			default:
			}
		}

		if mainWorker {
		drain:
			for {
				select {
				case v, ok := <-h.logCh:
					if !ok {
						break drain
					}

					currentGlobalBuffer <- v
				default:
					break drain
				}
			}
		}
	}()

	var entry interface{}
	var ok bool
	var err error

   */
  @CanIgnoreReturnValue
  @J2ktIncompatible
  @GwtIncompatible // BlockingQueue
  @SuppressWarnings("GoodTime") // should accept a java.time.Duration
  public static <E> int drain(
      BlockingQueue<E> q,
      Collection<? super E> buffer,
      int numElements,
      long timeout,
      TimeUnit unit)
      throws InterruptedException {
    Preconditions.checkNotNull(buffer);

À la place, en étant "asynchrone", une fois terminée, une tâche peut légèrement attendre (quelques microsecondes) que l'ordinateur / le programme finisse ce qu'il était en train de faire, et revienne récupérer le résultat.