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`

/// note | Détails techniques

ASGI est une nouvelle "norme" développée par les membres de l'équipe principale de Django. Il ne s'agit pas encore d'une "norme Python" (un PEP), bien qu'ils soient en train de le faire.

        List<String> trail = new ArrayList<>(1);
        trail.add(result.getId());
        result.setDependencyTrail(trail);

        return result;
    }

    public static void toArtifacts(
            Collection<org.apache.maven.artifact.Artifact> artifacts,
            Collection<? extends DependencyNode> nodes,
            List<String> trail,
            DependencyFilter filter) {

			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()

            @Nullable Throwable error) {}

    /**
     * If this is an accumulating log, it will "drain" this instance. It returns the accumulated log entries, and
     * also "resets" this instance to empty (initial) state.
     */
    @Nonnull
    default List<Entry> drain() {
        return List.of();
    }

# 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/...",

	}

	bp.Put(b)

	// Check the size of the pool.
	if uint64(len(bp.c)) != size {
		t.Fatalf("bytepool size invalid: got %v want %v", len(bp.c), size)
	}

	// lets drain the buf channel first before we validate invalid buffers.
	for range size {
		bp.Get() // discard
	}

	// Try putting some invalid buffers into pool

	// ReplicateQueued - replication being queued trail
	ReplicateQueued = "replicate:queue"

	// ReplicateExisting - audit trail for existing objects replication
	ReplicateExisting = "replicate:existing"
	// ReplicateExistingDelete - audit trail for delete replication triggered for existing delete markers
	ReplicateExistingDelete = "replicate:existing:delete"

	<-serverSent

	// Now do 100 other requests to ensure that the server doesn't block.
	for range 100 {
		_, err := remoteConn.Request(ctx, handlerTest2, []byte(testPayload))
		errFatal(err)
	}
	// Drain responses
	got := 0
	for resp := range st.responses {
		// t.Log("got response", resp)
		errFatal(resp.Err)
		if resp.Msg[0] != byte(got) {
			t.Error("expected response", got, "got", resp.Msg[0])
		}