/**
 * Represents the result of a search operation in the rank fusion system.
 *
 * This class encapsulates all the information returned from a search query,
 * including the list of matching documents, total record count, query execution
 * time, facet information, and metadata about the search results.
 */
public class SearchResult {

    /** The list of documents returned by the search query. */

    // names or patterns, but they should be treated as absolute. At the same time, any hostname
    // presented to this method should also be treated as absolute for the purposes of matching
    // to the server certificate.
    //   www.android.com  matches www.android.com
    //   www.android.com  matches www.android.com.
    //   www.android.com. matches www.android.com.

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

     * Gets JAR files from the WEB-INF/lib directory that start with the specified prefix.
     *
     * @param namePrefix the prefix that JAR file names should start with
     * @return an array of File objects representing matching JAR files, or empty array if none found
     */
    public static File[] getJarFiles(final String namePrefix) {
        final ServletContext context = LaServletContextUtil.getServletContext();
        if (context == null) {

  // ExtendSessionGraphHelper manually.
  std::atomic<bool> extend_before_run;
};

struct TF_ImportGraphDefOptions {
  tensorflow::ImportGraphDefOptions opts;

  // Backing memory for TensorId fields in opts.
  // TODO(skyewm): it'd be better if ImportGraphDefOptions owned this.
  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64
# Note: this is not set to "--cpu", because that changes the package name
# to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow"
# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-arm64:latest

# OAuth2 с паролем (и хешированием), Bearer с JWT-токенами { #oauth2-with-password-and-hashing-bearer-with-jwt-tokens }

Теперь, когда у нас определен процесс обеспечения безопасности, давайте сделаем приложение действительно безопасным, используя токены <abbr title="JSON Web Tokens – веб‑токены JSON">JWT</abbr> и безопасное хеширование паролей.

Этот код можно реально использовать в своем приложении, сохранять хэши паролей в базе данных и т.д.

        SmbFile file = mock(SmbFile.class);
        when(file.getAttributes()).thenReturn(SmbFile.ATTR_HIDDEN);
        assertFalse(filter.accept(file), "Attributes not matching should be rejected");
        verify(file, times(1)).getAttributes();
    }

    @Test
    public void testAcceptIgnoresWildcard() throws Exception {
        // Wildcard shouldn't affect accept logic.

    Lookup getLookup();

    /**
     * The TCCL, if implementation requires it.
     */
    @Nonnull
    Optional<ClassLoader> currentThreadClassLoader();

    /**
     * Performs a clean shutdown of backing container.
     */
    @Override
    void close() throws Exception;

public final class RemovalListeners {

  private RemovalListeners() {}

  /**
   * Returns a {@code RemovalListener} which processes all eviction notifications using {@code
   * executor}.
   *
   * @param listener the backing listener
   * @param executor the executor with which removal notifications are asynchronously executed
   */
  public static <K, V> RemovalListener<K, V> asynchronous(
      RemovalListener<K, V> listener, Executor executor) {