throws ArtifactResolutionException, ArtifactNotFoundException {
        ArtifactResolutionResult result = resolve(request);

        // We have collected all the problems so let's mimic the way the old code worked and just blow up right here.
        // That's right lets just let it rip right here and send a big incomprehensible blob of text at unsuspecting
        // users. Bad dog!

## Bedingte OpenAPI aus Einstellungen und Umgebungsvariablen { #conditional-openapi-from-settings-and-env-vars }

Sie können problemlos dieselben Pydantic-Einstellungen verwenden, um Ihre generierte OpenAPI und die Dokumentationsoberflächen zu konfigurieren.

Zum Beispiel:

{* ../../docs_src/conditional_openapi/tutorial001_py310.py hl[6,11] *}

      // null?  no problem
    }

    public static void staticOneArgJsr305NullableCorrectlyDoesNotThrowNpe(
        @javax.annotation.Nullable String s) {
      // null?  no problem
    }

    public static void staticOneArgNullableCorrectlyDoesNotThrowNpe(@Nullable String s) {
      // null?  no problem
    }

   * reading, such as an {@code InputStream}. Unlike with writable resources, there's no chance that
   * a failure that occurs when closing the stream indicates a meaningful problem such as a failure
   * to flush all bytes to the underlying resource.
   *
   * @param inputStream the input stream to be closed, or {@code null} in which case this method
   *     does nothing
   * @since 17.0

So, you can declare your function as:

{* ../../docs_src/path_params_numeric_validations/tutorial002_py310.py hl[7] *}

But keep in mind that if you use `Annotated`, you won't have this problem, it won't matter as you're not using the function parameter default values for `Query()` or `Path()`.

{* ../../docs_src/path_params_numeric_validations/tutorial002_an_py310.py *}

    }

    @Override
    public Optional<Long> sizeIfKnown() {
      if (!(sources instanceof Collection)) {
        // Infinite Iterables can cause problems here. Of course, it's true that most of the other
        // methods on this class also have potential problems with infinite  Iterables. But unlike
        // those, this method can cause issues even if the user is dealing with a (finite) slice()

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

### Multiple Processes - An Example { #multiple-processes-an-example }

In this example, there's a **Manager Process** that starts and controls two **Worker Processes**.

Das steht im Gegensatz zu den **Entwicklungsphasen**, in denen Sie ständig den Code ändern, kaputt machen, reparieren, den Entwicklungsserver stoppen und neu starten, usw.

Ainsi, vous pouvez déclarer votre fonction comme suit :

{* ../../docs_src/path_params_numeric_validations/tutorial002_py310.py hl[7] *}

Mais gardez à l'esprit que si vous utilisez `Annotated`, vous n'aurez pas ce problème, cela n'aura pas d'importance car vous n'utilisez pas les valeurs par défaut des paramètres de fonction pour `Query()` ou `Path()`.

{* ../../docs_src/path_params_numeric_validations/tutorial002_an_py310.py *}

qui offre de bonnes performances, une bonne stabilité, _etc._, afin que vos **utilisateurs** puissent **accéder** à
l'application efficacement et sans interruption ni problème.

Ceci contraste avec les étapes de **développement**, où vous êtes constamment en train de modifier le code, de le casser
et de le réparer, d'arrêter et de redémarrer le serveur de développement, _etc._