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)

# 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",

	// 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

    ],
)

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 numElements,
      long timeout,
      TimeUnit unit,
      boolean interruptibly)
      throws InterruptedException {
    return interruptibly
        ? Queues.drain(q, buffer, numElements, timeout, unit)

  @GwtIncompatible // BlockingQueue
  @IgnoreJRERequirement // Users will use this only if they're already using Duration
  public static <E> int drain(
      BlockingQueue<E> q, Collection<? super E> buffer, int numElements, Duration timeout)
      throws InterruptedException {
    return drain(q, buffer, numElements, toNanosSaturated(timeout), NANOSECONDS);
  }

  /**

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

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

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

	lastBatchProcess := time.Now()

	buf := bytebufferpool.Get()

   */
  @CanIgnoreReturnValue
  @J2ktIncompatible
  @GwtIncompatible // BlockingQueue
  public static <E> int drain(
      BlockingQueue<E> q, Collection<? super E> buffer, int numElements, Duration timeout)
      throws InterruptedException {
    return drain(q, buffer, numElements, toNanosSaturated(timeout), NANOSECONDS);
  }

  /**

  url: https://github.com/nayeonkinn
pe-brian:
  login: pe-brian
  count: 3
  avatarUrl: https://avatars.githubusercontent.com/u/1783138?u=7e6242eb9e85bcf673fa88bbac9dd6dc3f03b1b5&v=4
  url: https://github.com/pe-brian
maxscheijen:
  login: maxscheijen
  count: 3
  avatarUrl: https://avatars.githubusercontent.com/u/47034840?v=4

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.