* within VarHandleAtomicHelper and AtomicReferenceFieldUpdaterAtomicHelper.
   *
   * Package-private "shouldn't" be necessary: The *AtomicHelper classes and AbstractFutureState
   * "should" be nestmates, so a call to MethodHandles.lookup or
   * AtomicReferenceFieldUpdater.newUpdater inside *AtomicHelper "should" have access to

Vous faites cela avec les types Python standard modernes.

Vous n'avez pas à apprendre une nouvelle syntaxe, les méthodes ou les classes d'une bibliothèque spécifique, etc.

Juste du **Python** standard.

Par exemple, pour un `int`:

```Python
item_id: int
```

ou pour un modèle `Item` plus complexe :

```Python

### Resumen

En resumen, declaras **una vez** los tipos de parámetros, body, etc. como parámetros de función.

Lo haces con tipos estándar modernos de Python.

No tienes que aprender una nueva sintaxis, los métodos o clases de un paquete específico, etc.

Solo **Python** estándar.

Por ejemplo, para un `int`:

```Python
item_id: int
```

o para un modelo `Item` más complejo:

```Python
item: Item

   * within VarHandleAtomicHelper and AtomicReferenceFieldUpdaterAtomicHelper.
   *
   * Package-private "shouldn't" be necessary: The *AtomicHelper classes and AbstractFutureState
   * "should" be nestmates, so a call to MethodHandles.lookup or
   * AtomicReferenceFieldUpdater.newUpdater inside *AtomicHelper "should" have access to

   * ThreadLocal; however, ThreadLocal is not well optimized for the case where the ThreadLocal is
   * non-static, and is initialized/removed frequently - this causes churn in the Thread specific
   * hashmaps. Using a static ThreadLocal to avoid that overhead would mean that different
   * ExecutionSequencer objects interfere with each other, which would be undesirable, in addition

        elapsedMillis + 5 >= expectedMillis);
  }

  // TODO(cpovirk): Split this into separate CountDownLatch and IncrementableCountDownLatch classes.

  /** Manages a {@link BlockingQueue} and associated timings for a {@code put} call. */
  private static final class TimedPutQueue {
    final BlockingQueue<String> queue;
    final Completion completed;

    /** Map from primitive types to wrapper types */
    protected static final Map<Class<?>, Class<?>> primitiveToWrapperMap = newHashMap();

    /** Map from primitive type names to classes */
    protected static final Map<String, Class<?>> primitiveNameToClassMap = newHashMap();

    static {
        wrapperToPrimitiveMap.put(Boolean.class, Boolean.TYPE);

### Recap { #recap }

In summary, you declare **once** the types of parameters, body, etc. as function parameters.

You do that with standard modern Python types.

You don't have to learn a new syntax, the methods or classes of a specific library, etc.

Just standard **Python**.

For example, for an `int`:

```Python
item_id: int
```

or for a more complex `Item` model:

```Python
item: Item
```

      return table.get(c);
    }

    @Override
    void setBits(BitSet bitSet) {
      bitSet.or(table);
    }
  }

  // Static constant implementation classes

  /** Implementation of {@link #any()}. */
  private static final class Any extends NamedFastMatcher {

    static final CharMatcher INSTANCE = new Any();

    private Any() {

@GwtCompatible
@NullMarked
public abstract class MapInterfaceTest<K extends @Nullable Object, V extends @Nullable Object>
    extends TestCase {

  /** A key type that is not assignable to any classes but Object. */
  private static final class IncompatibleKeyType {
    @Override
    public String toString() {
      return "IncompatibleKeyType";
    }
  }

  protected final boolean supportsPut;