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`

# 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/...",

	// 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"

# 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",

	d.maxSleep = maxWait
	d.cycle = make(chan struct{})
	return nil
}

const (
	// ILMExpiry - audit trail for ILM expiry
	ILMExpiry = "ilm:expiry"
	// ILMFreeVersionDelete - audit trail for ILM free-version delete
	ILMFreeVersionDelete = "ilm:free-version-delete"
	// ILMTransition - audit trail for ILM transitioning.
	ILMTransition = " ilm:transition"
)

    * 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**:

 *
 * Features:
 * - Structured JSON logging
 * - Sensitive data masking
 * - Event categorization
 * - Performance metrics
 * - Compliance-ready audit trail
 * - Asynchronous logging with bounded queue
 * - Object pooling to reduce GC pressure
 */
public class SecurityAuditLogger {

    private static final Logger auditLog = LoggerFactory.getLogger("SECURITY.AUDIT");

		}
		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.

		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) {