Ainsi, vous pouvez ajouter des données supplémentaires au schéma généré automatiquement.

Par exemple, vous pouvez décider de lire et de valider la requête avec votre propre code, sans utiliser les fonctionnalités automatiques de validation proposée par Pydantic, mais vous pouvez toujours définir la requête dans le schéma OpenAPI.

        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
        `SUM` divided by number of elements in losses. This reduction type is
        not supported when used with `tf.distribute.Strategy` outside of
        built-in training loops like `tf.keras` `compile`/`fit`.

  // limit, which is a limit on the number of requests of this
  // priority level that may be exeucting at a given time.  ACS must
  // be a positive number. The server's concurrency limit (SCL) is
  // divided among the concurrency-controlled priority levels in
  // proportion to their assured concurrency shares. This produces
  // the assured concurrency value (ACV) --- the number of requests

  // limit, which is a limit on the number of requests of this
  // priority level that may be exeucting at a given time.  ACS must
  // be a positive number. The server's concurrency limit (SCL) is
  // divided among the concurrency-controlled priority levels in
  // proportion to their assured concurrency shares. This produces
  // the assured concurrency value (ACV) --- the number of requests

  // limit, which is a limit on the number of requests of this
  // priority level that may be exeucting at a given time.  ACS must
  // be a positive number. The server's concurrency limit (SCL) is
  // divided among the concurrency-controlled priority levels in
  // proportion to their assured concurrency shares. This produces
  // the assured concurrency value (ACV) --- the number of requests

  // This is used both for requests dispatched from this priority level
  // as well as requests dispatched from other priority levels
  // borrowing seats from this level.
  // The server's concurrency limit (ServerCL) is divided among the
  // Limited priority levels in proportion to their NCS values:
  //
  // NominalCL(i)  = ceil( ServerCL * NCS(i) / sum_ncs )
  // sum_ncs = sum[limited priority level k] NCS(k)
  //

Levaria o mesmo tempo para finalizar com ou sem chamadas (concorrência) e você teria feito o mesmo tanto de trabalho.

Mas nesse caso, se você trouxesse os 8 ex-caixas / cozinheiros / agora-faxineiros, e cada um deles (mais você) pudessem dividir a casa para limpá-la, vocês fariam toda a limpeza em **paralelo**, com a ajuda extra, e terminariam muito mais cedo.

Nesse cenário, cada um dos faxineiros (incluindo você) poderia ser um processador, fazendo a sua parte do trabalho.

*Oui, c'est toute l'histoire*.

---

Il n'y a plus d'attente 🕙 nulle part, juste beaucoup de travail à effectuer, dans différentes pièces de la maison.

Vous pourriez diviser en différentes sections comme avec les burgers, d'abord le salon, puis la cuisine, etc. Mais vous n'attendez 🕙 rien, vous ne faites que nettoyer et nettoyer, la séparation en sections ne changerait rien au final.

### <a href="https://github.com/encode/apistar" class="external-link" target="_blank">APIStar</a> (<= 0.5)

Juste avant de décider de développer **FastAPI**, j'ai trouvé le serveur **APIStar**. Il contenait presque tout ce
que je recherchais et avait un beau design.

    ) {
      events.add(AssertionError("onReset"))
      notifyAll()
    }
  }

  companion object {
    fun roundUp(
      num: Int,
      divisor: Int,
    ): Int = (num + divisor - 1) / divisor

    val IGNORE =
      object : PushObserver {
        override fun onRequest(
          streamId: Int,
          requestHeaders: List<Header>,
        ) = false