Arrays.fill(result, oldSize, newSize, ABSENT);
    return result;
  }

  @Override
  public int size() {
    return size;
  }

  /**
   * Ensures that all of the internal structures in the HashBiMap are ready for this many elements.
   */
  private void ensureCapacity(int minCapacity) {
    if (nextInBucketKToV.length < minCapacity) {
      int oldCapacity = nextInBucketKToV.length;

  }

  /**
   * Creates a {@code CompactLinkedHashMap} instance, with a high enough "initial capacity" that it
   * <i>should</i> hold {@code expectedSize} elements without rebuilding internal data structures.
   *
   * @param expectedSize the number of elements you expect to add to the returned set
   * @return a new, empty {@code CompactLinkedHashMap} with enough capacity to hold {@code

    * Parce que les structures de données de pydantic consistent seulement en une instance de classe que vous définissez; l'auto-complétion, le linting, mypy et votre intuition devrait être largement suffisante pour valider vos données.
* Valide les **structures complexes**:
    * Utilise les modèles hiérarchique de Pydantic, le `typage` Python pour les `Lists`, `Dict`, etc.

                        return listOfNotNull(symbolBuilder.createPackageSymbolIfOneExists(classId.packageFqName))
                    } else {
                        // This is unexpected. The code probably contains some weird structures. In this case, we just fail the resolution
                        // with zero results.
                        return emptyList()
                    }
                }

     */
    abstract SetBuilderImpl<E> copy();

    /**
     * Call this before build(). Does a final check on the internal data structures, e.g. shrinking
     * unnecessarily large structures or detecting previously unnoticed hash flooding.
     */
    SetBuilderImpl<E> review() {
      return this;
    }

    abstract ImmutableSet<E> build();
  }

* `int`
* `float`
* `bool`
* `bytes`

```Python hl_lines="1"
{!../../../docs_src/python_types/tutorial005.py!}
```

### Types génériques avec des paramètres de types

Il existe certaines structures de données qui contiennent d'autres valeurs, comme `dict`, `list`, `set` et `tuple`. Et les valeurs internes peuvent elles aussi avoir leurs propres types.

 * dispersion is easily corrected using a secondary hash function (which all reasonable hash table
 * implementations in Java use). For the many uses of hash functions beyond data structures,
 * however, {@code Object.hashCode} almost always falls short -- hence this library.
 *
 * @author Kevin Bourrillion
 * @since 11.0
 */
@Immutable
@ElementTypesAreNonnullByDefault

import okio.buffer

/**
 * Caches HTTP and HTTPS responses to the filesystem so they may be reused, saving time and
 * bandwidth.
 *
 * The Cache instance must have exclusive access to the [directory], since the internal data structures
 * may cause corruption or runtime errors if not. It may however be shared amongst multiple OkHttpClient
 * instances.
 *
 * ## Cache Optimization
 *

    Builder(int estimatedDistinct) {
      this.contents = ObjectCountHashMap.createWithExpectedSize(estimatedDistinct);
    }

    Builder(boolean forSubtype) {
      // for ImmutableSortedMultiset not to allocate data structures not used there
      this.contents = null;
    }

    /**
     * Adds {@code element} to the {@code ImmutableMultiset}.
     *
     * @param element the element to add

* `int`
* `float`
* `bool`
* `bytes`

```Python hl_lines="1"
{!../../../docs_src/python_types/tutorial005.py!}
```

### Generic types with type parameters

There are some data structures that can contain other values, like `dict`, `list`, `set` and `tuple`. And the internal values can have their own type too.