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

    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

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 %. *

        if (topMetaIterator == null) {
          return false;
        }

        iterator = topMetaIterator.next();

        if (iterator instanceof ConcatenatedIterator) {
          // Instead of taking linear time in the number of nested concatenations, unpack
          // them into the queue
          @SuppressWarnings("unchecked")
          ConcatenatedIterator<T> topConcat = (ConcatenatedIterator<T>) iterator;