import org.dbflute.optional.OptionalThing;
import org.lastaflute.taglib.function.LaFunctions;
import org.lastaflute.web.response.ActionResponse;
import org.lastaflute.web.response.StreamResponse;
import org.lastaflute.web.ruts.process.ActionRuntime;
import org.lastaflute.web.util.LaRequestUtil;
import org.lastaflute.web.util.LaServletContextUtil;
import org.opensearch.core.common.text.Text;
import org.opensearch.search.fetch.subphase.highlight.HighlightField;

<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
<span style="color: green;">INFO</span>:     Started reloader process [28720]
<span style="color: green;">INFO</span>:     Started server process [28722]
<span style="color: green;">INFO</span>:     Waiting for application startup.
<span style="color: green;">INFO</span>:     Application startup complete.
```

</div>

    }

    /**
     * Processes the OpenSearch response and converts it to a SearchResult.
     *
     * @param searchResponseOpt the optional search response from OpenSearch
     * @return the processed search result
     */
    protected SearchResult processResponse(final OptionalEntity<SearchResponse> searchResponseOpt) {
        final FessConfig fessConfig = ComponentUtil.getFessConfig();

 *
 * The task runner is also responsible for releasing held threads when the library is unloaded.
 * This is for the benefit of container environments that implement code unloading.
 *
 * Most applications should share a process-wide [TaskRunner] and use queues for per-client work.
 */
class TaskRunner(
  val backend: Backend,
  internal val logger: Logger = TaskRunner.logger,
) : Lockable {
  private var nextQueueName = 10000

 * (or {@link FluentFuture#transform(com.google.common.base.Function, Executor)
 * FluentFuture.transform}), but you will often find it easier to use a framework. Frameworks
 * automate the process, often adding features like monitoring, debugging, and cancellation.
 * Examples of frameworks include:
 *
 * <ul>
 *   <li><a href="https://dagger.dev/producers.html">Dagger Producers</a>
 * </ul>
 *

    this.backingThreadFactory = checkNotNull(backingThreadFactory);
    return this;
  }

  /**
   * Returns a new thread factory using the options supplied during the building process. After
   * building, it is still possible to change the options used to build the ThreadFactory and/or
   * build again. State is not shared amongst built instances.
   *

    checkArgument(isFinite(x1) && isFinite(y1));
    return new LinearTransformationBuilder(x1, y1);
  }

  /**
   * This is an intermediate stage in the construction process. It is returned by {@link
   * LinearTransformation#mapping}. You almost certainly don't want to keep instances around, but
   * instead use method chaining. This represents a single point mapping, i.e. a mapping between one

        decodedName.decode(decodeBuffer);

        // Verify the decoded data matches
        assertEquals(translatedName.sid_type, decodedName.sid_type);
        // The decode process doesn't preserve the original length, it reads from the buffer
        assertNotNull(decodedName.name);
        assertNotNull(decodedName.name.buffer);
        // Buffer length is based on maximum_length/2 from the encoded data

#### Shared reader thread

E também depois que a `response` é gerada, antes de retorná-la.

Por exemplo, você pode adicionar um cabeçalho personalizado `X-Process-Time` contendo o tempo em segundos que levou para processar a solicitação e gerar uma resposta:

{* ../../docs_src/middleware/tutorial001.py hl[10,12:13] *}

## Outros middlewares