RateLimiter rateLimiter = RateLimiter.create(1.0, stopwatch);
    rateLimiter.acquire(1); // no wait
    rateLimiter.acquire(1); // R1.00, to repay previous

    rateLimiter.setRate(2.0); // update the rate!

    rateLimiter.acquire(1); // R1.00, to repay previous (the previous was under the old rate!)
    rateLimiter.acquire(2); // R0.50, to repay previous (now the rate takes effect)

    RateLimiter rateLimiter = RateLimiter.create(1.0, stopwatch);
    rateLimiter.acquire(1); // no wait
    rateLimiter.acquire(1); // R1.00, to repay previous

    rateLimiter.setRate(2.0); // update the rate!

    rateLimiter.acquire(1); // R1.00, to repay previous (the previous was under the old rate!)
    rateLimiter.acquire(2); // R0.50, to repay previous (now the rate takes effect)

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---

When building APIs, you normally use these specific HTTP methods to perform a specific action.

Normally you use:

* `POST`: to create data.
* `GET`: to read data.
* `PUT`: to update data.
* `DELETE`: to delete data.

So, in OpenAPI, each of the HTTP methods is called an "operation".

We are going to call them "**operations**" too.

Normalerweise verwenden Sie:

* `POST`: um Daten zu erzeugen (create).
* `GET`: um Daten zu lesen (read).
* `PUT`: um Daten zu aktualisieren (update).
* `DELETE`: um Daten zu löschen (delete).

In OpenAPI wird folglich jede dieser HTTP-Methoden als „Operation“ bezeichnet.

Wir werden sie auch „**Operationen**“ nennen.

      output_gradients, result, /*build_default_zeros_grads*/ false));
  return absl::OkStatus();
}

// Helper functions which delegate to `AbstractOperation`, update
// the state of the ForwardOperation and call the tape as appropriate.
// These APIs are mainly to facilitate testing and are subject to change.
namespace internal {
absl::Status Reset(AbstractOperation* op_, const char* op,

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Utiliser le paramètre `update` de Pydantic { #using-pydantics-update-parameter }

Vous pouvez maintenant créer une copie du modèle existant avec `.model_copy()`, et passer le paramètre `update` avec un `dict` contenant les données à mettre à jour.

Comme `stored_item_model.model_copy(update=update_data)` :

{* ../../docs_src/body_updates/tutorial002_py310.py hl[33] *}

- Updated etcd image to `3.5.9-0`. ([#117999](https://github.com/kubernetes/kubernetes/pull/117999), [@kkkkun](https://github.com/kkkkun)) [SIG API Machinery]
- Updated cAdvisor to `v0.47.2` and fixed metrics in `cri-o` when a container restarts. ([#118774](https://github.com/kubernetes/kubernetes/pull/118774), [@harche](https://github.com/harche))

	metric.Avg = metric.Sum / float64(metric.Count)
	subsysMetrics[key] = metric

	resourceMetricsMap[subSys] = subsysMetrics
}

// updateDriveIOStats - Updates the drive IO stats by calculating the difference between the current and latest updated values.
func updateDriveIOStats(currentStats madmin.DiskIOStats, latestStats madmin.DiskIOStats, labels map[string]string) {
	sectorSize := uint64(512)
	kib := float64(1 << 10)

/// tip

To declare a query parameter with a type of `list`, like in the example above, you need to explicitly use `Query`, otherwise it would be interpreted as a request body.

///

The interactive API docs will update accordingly, to allow multiple values:

<img src="/img/tutorial/query-params-str-validations/image02.png">

### Query parameter list / multiple values with defaults { #query-parameter-list-multiple-values-with-defaults }