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.

      used to shard the checkpoint with a maximum shard file size. Users with
      advanced use cases can also write their own custom
      `tf.train.experimental.ShardingCallback`s.

* `tf.train.CheckpointOptions`
    * Added `experimental_skip_slot_variables` (a boolean option) to skip
    restoring of optimizer slot variables in a checkpoint.

*   `tf.saved_model.SaveOptions`

  @CanIgnoreReturnValue
  @J2ktIncompatible
  @GwtIncompatible // BlockingQueue
  public static <E> int drain(
      BlockingQueue<E> q, Collection<? super E> buffer, int numElements, Duration timeout)
      throws InterruptedException {
    // TODO(b/126049426): Consider using saturateToNanos(timeout) instead.
    return drain(q, buffer, numElements, timeout.toNanos(), NANOSECONDS);
  }

  /**

  @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 {
    // TODO(b/126049426): Consider using saturateToNanos(timeout) instead.
    return drain(q, buffer, numElements, timeout.toNanos(), NANOSECONDS);
  }

  /**

# TODO(b/173549186): Move Google-internal TF code out of learning/brain
package_group(
    name = "internal",
    packages = [
        "//devtools/python/indexer/...",
        "//learning/brain/keras/...",
        "//learning/brain/mlir/...",
        "//learning/brain/tfrt/...",
        "//learning/lib/ami/simple_ml/...",
        "//learning/pathways/...",

# 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",

    * If you know Python types you know how to use Pydantic.
* Plays nicely with your **<abbr title="Integrated Development Environment, similar to a code editor">IDE</abbr>/<abbr title="A program that checks for code errors">linter</abbr>/brain**:
    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:

		}
		h.mutex.Unlock()
	case <-h.ctx.Done():
		h.mutex.Lock()
		h.endTime = UTCNow()
		h.currentStatus.Summary = healFinishedStatus
		h.mutex.Unlock()

		// drain traverse channel so the traversal
		// go-routine does not leak.
		go func() {
			// Eventually the traversal go-routine closes
			// the channel and returns, so this go-routine
			// itself will not leak.

     */
    int threshold;

    /** The per-segment table. */
    volatile @Nullable AtomicReferenceArray<E> table;

    /**
     * A counter of the number of reads since the last write, used to drain queues on a small
     * fraction of read operations.
     */
    final AtomicInteger readCount = new AtomicInteger();

    Segment(MapMakerInternalMap<K, V, E, S> map, int initialCapacity) {

		minDisks = er.setDriveCount - er.defaultParityCount
	}

	if minDisks == er.setDriveCount/2 {
		// when data and parity are same we must atleast
		// wait for response from 1 extra drive to avoid
		// split-brain.
		minDisks++
	}

	calcQuorum := func(metaArr []FileInfo, errs []error) (FileInfo, []FileInfo, []StorageAPI, time.Time, string, error) {