*
   * <p>If {@code expectedSize} is exactly the number of elements added to the builder before {@link
   * Builder#build} is called, the builder is likely to perform better than an unsized {@link
   * #builder()} would have.
   *
   * <p>It is not specified if any performance benefits apply if {@code expectedSize} is close to,
   * but not exactly, the number of elements added to the builder.

    // do n swaps, minus a delta (0 or 2 for Reversal, gcd(d, n) for Successive), so that's about
    // twice as many reads and writes. But benchmarking shows that they usually perform better than
    // Dolphin. Reversal is about as good as Successive on average, and it is much simpler,
    // especially since we already have a `reverse` method.
    checkNotNull(array);

   * structure (to make it possible to throw ConcurrentModificationException in the iterator). Note
   * that we choose not to make this volatile, so we do less of a "best effort" to track such
   * errors, for better performance.
   *
   * <p>For a new instance, where the arrays above have not yet been allocated, the value of {@code
   * metadata} is the size that the arrays should be allocated with. Once the arrays have been

   *
   * <p>If {@code keys} is a {@link Set}, a live view can be obtained instead of a copy using {@link
   * Maps#asMap(Set, Function)}.
   *
   * <p><b>Note:</b> on Java 8+, it is usually better to use streams. For example:
   *
   * {@snippet :
   * import static com.google.common.collect.ImmutableMap.toImmutableMap;
   * ...
   * ImmutableMap<Color, String> colorNames =

   *
   * <p>If {@code keys} is a {@link Set}, a live view can be obtained instead of a copy using {@link
   * Maps#asMap(Set, Function)}.
   *
   * <p><b>Note:</b> on Java 8+, it is usually better to use streams. For example:
   *
   * {@snippet :
   * import static com.google.common.collect.ImmutableMap.toImmutableMap;
   * ...
   * ImmutableMap<Color, String> colorNames =

Eso, más el simple hecho de que Python es el lenguaje principal para **Data Science**, Machine Learning y especialmente Deep Learning, hacen de FastAPI una muy buena opción para APIs web de Data Science / Machine Learning y aplicaciones (entre muchas otras).

## Changelog since v1.5.0-beta.2

### Other notable changes

* Better compat with very old iptables (e.g. CentOS 6) ([#37594](https://github.com/kubernetes/kubernetes/pull/37594), [@thockin](https://github.com/thockin))

   * {@link TreeSet} or the {@link Map#keySet} of an {@code IdentityHashMap}.
   *
   * <p><b>Note:</b> The returned view performs slightly better when {@code set1} is the smaller of
   * the two sets. If you have reason to believe one of your sets will generally be smaller than the
   * other, pass it first. Unfortunately, since this method sets the generic type of the returned

## Technische Details { #technical-details }

Moderne Versionen von Python unterstützen **„asynchronen Code“** unter Verwendung sogenannter **„Coroutinen“** mithilfe der Syntax **`async` und `await`**.

Nehmen wir obigen Satz in den folgenden Abschnitten Schritt für Schritt unter die Lupe:

* **Asynchroner Code**
* **`async` und `await`**
* **Coroutinen**

## Asynchroner Code { #asynchronous-code }

        // condition efficiently without _introducing_ deadlock is probably
        // tricky. For now, just accept the race condition---missing a warning
        // now and then is still better than having no deadlock detection.
        allowedPriorLocks.put(acquiredLock, new ExampleStackTrace(acquiredLock, this));
      } else {
        // Unsafe acquisition order detected. Create and cache a