A pull request is not a finished product—it is the start of a conversation, and an implied contract: we promise to provide thoughtful feedback, and we expect the contributor to engage with us in return.

## What we expect from contributors

Et c'est le même niveau de performance que celui obtenu avec **FastAPI**.

 *       <ul>
 *         <li>2 levels: average of 64ns per lock()/unlock()
 *         <li>3 levels: average of 77ns per lock()/unlock()
 *         <li>4 levels: average of 99ns per lock()/unlock()
 *         <li>5 levels: average of 103ns per lock()/unlock()
 *         <li>10 levels: average of 184ns per lock()/unlock()
 *         <li>20 levels: average of 393ns per lock()/unlock()
 *       </ul>
 * </ul>
 *

 * you would expect.
 *
 * <p>If required to do a weighted average between an infinity and a finite value, or between an
 * infinite value and itself, the infinite value is returned. If required to do a weighted average
 * between {@link Double#NEGATIVE_INFINITY NEGATIVE_INFINITY} and {@link Double#POSITIVE_INFINITY

* `expose_headers` – Angabe der Responseheader, auf die der Browser zugreifen können soll. Standardmäßig `[]`.
* `max_age` – Legt eine maximale Zeit in Sekunden fest, die Browser CORS-Responses zwischenspeichern dürfen. Standardmäßig `600`.

</blockquote>

## Recherche { #investigation }

- **Documentation**: The [Gradle User Manual](https://docs.gradle.org/current/userguide/userguide.html) covers everything from basic to advanced configurations.
- **Community Forum**: Engage with others on the [Gradle Forum](https://discuss.gradle.org/) for discussions, tips, and best practices.

  // We could make this a Map<E, EndpointPair<N>>. It would make incidentNodes(edge) slightly
  // faster, but also make Networks consume 5 to 20+% (increasing with average degree) more memory.
  final MapIteratorCache<E, N> edgeToReferenceNode; // referenceNode == source if directed

  /** Constructs a graph with the properties specified in {@code builder}. */

      assertEquivalent(stats.mean(), stream(array).average().getAsDouble());
      assertEquivalent(stats.sum(), stream(array).sum());
      assertEquivalent(stats.max(), stream(array).max().getAsDouble());
      assertEquivalent(stats.min(), stream(array).min().getAsDouble());
      // static method on Stats vs on instance method on DoubleStream
      assertEquivalent(Stats.meanOf(array), stream(array).average().getAsDouble());

Avec **FastAPI** vous obtenez toutes les fonctionnalités de **Pydantic** (puisque FastAPI est basé sur Pydantic pour toute la gestion des données) :

* **Pas de prise de tête** :
    * Pas de micro-langage de définition de schéma à apprendre.
    * Si vous connaissez les types Python vous savez utiliser Pydantic.