Isso pode ser muito útil para configurar **recursos** que você precisa usar por toda a aplicação, e que são **compartilhados** entre as requisições e/ou que você precisa **limpar** depois. Por exemplo, um pool de conexões com o banco de dados ou o carregamento de um modelo de Aprendizado de Máquina compartilhado.

## Caso de uso { #use-case }

Vamos começar com um exemplo de **caso de uso** e então ver como resolvê-lo com isso.

 * This processor supports searching with multiple searchers concurrently and merging their results based on
 * ranking scores to provide more comprehensive and accurate search results.
 *
 * The processor maintains a pool of searchers and an executor service for concurrent operations.
 * It implements rank fusion techniques to combine results from different search engines
 * and provides a unified search interface.
 */

```Python
contents = myfile.file.read()
```

/// note | Détails techniques `async`

Lorsque vous utilisez les méthodes `async`, **FastAPI** exécute les méthodes de fichier dans un pool de threads et les attend.

///

/// note | Détails techniques Starlette

        new LinkedBlockingDeque<Object>(),
        new LinkedBlockingDeque<Object>(10),
        new PriorityBlockingQueue<Object>(10, Ordering.arbitrary()));
  }

  /*
   * We need to perform operations in a thread pool, even for simple cases, because the queue might
   * be a SynchronousQueue.
   */
  private ExecutorService threadPool;

  @Override
  public void setUp() {
    threadPool = newCachedThreadPool();
  }

Cela peut être très utile pour configurer des **ressources** dont vous avez besoin pour l'ensemble de l'application, qui sont **partagées** entre les requêtes, et/ou que vous devez **nettoyer** ensuite. Par exemple, un pool de connexions à une base de données, ou le chargement d'un modèle d'apprentissage automatique partagé.

## Cas d'utilisation { #use-case }

      long d = ns - (System.nanoTime() - startTime);
      if (d > 0L) {
        millis = d / (1000 * 1000);
      } else {
        break;
      }
    }
  }

  /** Waits out termination of a thread pool or fails doing so. */
  final void joinPool(ExecutorService exec) throws InterruptedException {
    try {
      exec.shutdown();
      assertTrue(
          "ExecutorService did not terminate in a timely manner",

Esto puede ser muy útil para configurar **recursos** que necesitas usar para toda la app, y que son **compartidos** entre requests, y/o que necesitas **limpiar** después. Por ejemplo, un pool de conexiones a una base de datos, o cargando un modelo de machine learning compartido.

## Caso de Uso { #use-case }

Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto.

 *  New: `ConnectionPool.setPolicy()` configures a minimum connection pool size for a target
    address. Use this to proactively open HTTP connections.

    Connections opened to fulfill this policy are subject to the connection pool's
    `keepAliveDuration` but do not count against the pool-wide `maxIdleConnections` limit.

    private static final Pattern IP_PATTERN = Pattern.compile("\\b(?:[0-9]{1,3}\\.){3}[0-9]{1,3}\\b");

    // Object pools for performance optimization
    private final Queue<AuditEntry> auditEntryPool = new ConcurrentLinkedQueue<>();
    private final Queue<StringBuilder> stringBuilderPool = new ConcurrentLinkedQueue<>();

    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by: