}
      return false;
    }

    @Override
    public int hashCode() {
      return Arrays.hashCode(functions);
    }
  }

  /**
   * Linear CongruentialGenerator to use for consistent hashing. See
   * http://en.wikipedia.org/wiki/Linear_congruential_generator
   */
  private static final class LinearCongruentialGenerator {
    private long state;

    public LinearCongruentialGenerator(long seed) {

  private static final int CUTOFF = (int) (MAX_TABLE_SIZE * DESIRED_LOAD_FACTOR);

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing
   * with linear probing in its implementation. The returned size is the smallest power of two that
   * can hold setSize elements with the desired load factor. Always returns at least setSize + 2.
   */
  @VisibleForTesting

   * equivalently, if this inverse view contains a key that is equal to {@code value}).
   *
   * <p>Due to the property that values in a BiMap are unique, this will tend to execute in
   * faster-than-linear time.
   *
   * @param value the object to search for in the values of this BiMap
   * @return true if a mapping exists from a key to the specified value
   */
  @Override

1003B         ; disallowed                             # NA   <reserved-1003B>
1003C..1003D  ; valid                                  # 4.0  LINEAR B SYLLABLE B017 ZA..LINEAR B SYLLABLE B074 ZE
1003E         ; disallowed                             # NA   <reserved-1003E>
1003F..1004D  ; valid                                  # 4.0  LINEAR B SYLLABLE B020 ZO..LINEAR B SYLLABLE B091 TWO

Ce type d'asynchronicité est ce qui a rendu NodeJS populaire (bien que NodeJS ne soit pas parallèle) et c'est la force du Go en tant que langage de programmation.

Et c'est le même niveau de performance que celui obtenu avec **FastAPI**.

labels.lastAccessTime	=	Dernier accès
labels.notificationTo	=	Notification à
labels.num	=	Numéro
labels.pn	=	Numéro de page
labels.protocolScheme	=	Schème
labels.purgeByBots	=	Purge par les bots
labels.logLevel	=	Niveau de journalisation
labels.purgeSearchLogDay	=	Purger le journal de recherche
labels.query	=	Requête
labels.queryId	=	ID de requête
labels.rt	=	rt
labels.searchLog	=	Journal de recherche
labels.sort	=	Trier

 *       {@link #poll} and {@link #remove()}) run in {@code O(log n) time}.
 *   <li>The {@link #remove(Object)} and {@link #contains} operations require linear ({@code O(n)})
 *       time.
 *   <li>If you only access one end of the queue, and don't use a maximum size, this class is
 *       functionally equivalent to {@link PriorityQueue}, but significantly slower.
 * </ul>
 *

Les principales fonctionnalités sont :

* **Rapidité** : De très hautes performances, au niveau de **NodeJS** et **Go** (grâce à Starlette et Pydantic). [L'un des frameworks Python les plus rapides](#performance).
* **Rapide à coder** : Augmente la vitesse de développement des fonctionnalités d'environ 200 % à 300 %. *

    It significantly reduces the peak memory required and latency incurred
    during restore.
*   Added a new library for library of matrix-free (iterative) solvers for
    linear equations, linear least-squares, eigenvalues and singular values in
    tensorflow/contrib/solvers. Initial version has lanczos bidiagonalization,
    conjugate gradients and CGLS.

      return entry2.getCount() - entry1.getCount(); // subtracting two nonnegative integers
    }
  }

  /**
   * An {@link AbstractMultiset} with additional default implementations, some of them linear-time
   * implementations in terms of {@code elementSet} and {@code entrySet}.
   */
  private abstract static class ViewMultiset<E extends @Nullable Object>
      extends AbstractMultiset<E> {
    @Override