throws Exception {
        File localRepoDir = new File(projectBuildingRequest.getLocalRepository().getBasedir());
        LocalRepository localRepo = new LocalRepository(localRepoDir, "simple");

        RepositorySystemSession session = new MavenSessionBuilderSupplier(repositorySystem)
                .get()
                .withLocalRepositories(localRepo)
                .build();

     * <p>
     * The implementation may optimize the execution by:
     * <ul>
     *   <li>Returning cached results for previously executed requests</li>
     *   <li>Grouping similar requests for batch processing</li>
     *   <li>Processing requests in parallel where appropriate</li>
     * </ul>
     *
     * @param <REQ> The request type
     * @param <REP> The response type

### Autres formes de code asynchrone

L'utilisation d'`async` et `await` est relativement nouvelle dans ce langage.

Mais cela rend la programmation asynchrone bien plus simple.

Cette même syntaxe (ou presque) était aussi incluse dans les versions modernes de Javascript (dans les versions navigateur et NodeJS).

Mais avant ça, gérer du code asynchrone était bien plus complexe et difficile.

        assertTrue(result.contains(Hexdump.toHexString(TEST_INFO_LEVEL, 3)));
    }

    @Test
    void testToStringWithDifferentPaths() {
        // Test with various path formats
        String[] testPaths = { "simple.txt", "path/to/file.doc", "//server/share/file.txt", "folder\\windows\\style.txt", "" // empty path
        };

        for (String path : testPaths) {

Nunca almacenes contraseñas de usuarios en texto plano. Siempre almacena un "hash seguro" que puedas verificar luego.

Si no lo sabes, aprenderás qué es un "hash de contraseña" en los [capítulos de seguridad](security/simple-oauth2.md#password-hashing){.internal-link target=_blank}.

///

## Múltiples modelos

Aquí tienes una idea general de cómo podrían ser los modelos con sus campos de contraseña y los lugares donde se utilizan:

  ZERO(0),
  /** Test a one-element collection. */
  ONE(1),
  /** Test a three-element collection. */
  SEVERAL(3),
  /*
   * TODO: add VERY_LARGE, noting that we currently assume that the fourth
   * sample element is not in any collection
   */

  ANY(ZERO, ONE, SEVERAL);

  private final Set<Feature<? super Collection>> implied;
  private final @Nullable Integer numElements;

  CollectionSize(int numElements) {

  ZERO(0),
  /** Test a one-element collection. */
  ONE(1),
  /** Test a three-element collection. */
  SEVERAL(3),
  /*
   * TODO: add VERY_LARGE, noting that we currently assume that the fourth
   * sample element is not in any collection
   */

  ANY(ZERO, ONE, SEVERAL);

  private final Set<Feature<? super Collection>> implied;
  private final @Nullable Integer numElements;

  CollectionSize(int numElements) {

import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.RangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.sampler.SamplerAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.SignificantTermsAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.TermsAggregationBuilder;

Mas você também pode explorar os benefícios do paralelismo e multiprocessamento (tendo múltiplos processadores rodando em paralelo) para trabalhos **limitados por CPU** como aqueles em sistemas de Machine Learning.

Isso, somado ao simples fato que Python é a principal linguagem para **Data Science**, Machine Learning e especialmente Deep Learning, faz do FastAPI uma ótima escolha para APIs web e aplicações com Data Science / Machine Learning (entre muitas outras).

     */
    @ValidateTypeFailure
    public Integer offset;

    /**
     * The page number for pagination.
     */
    @ValidateTypeFailure
    public Integer pn;

    /**
     * Similar document hash for finding related documents.
     */
    @Size(max = 1000)
    public String sdh;

    /**
     * Parameter to control tracking of total hits in search results.
     */
    @Size(max = 100)