def model2 = models2.right
        model2.size() == 2
        model2[0].message == "It works from project :"
        model2[1].message == "It works from project :a"

        and:
        fixture.assertStateLoaded()
        outputDoesNotContain("creating model")
    }

    def "caches execution of phased BuildAction that queries custom tooling model and that runs tasks"() {
        given:

# Extramodelle

Fahren wir beim letzten Beispiel fort. Es gibt normalerweise mehrere zusammengehörende Modelle.

Insbesondere Benutzermodelle, denn:

* Das **hereinkommende Modell** sollte ein Passwort haben können.
* Das **herausgehende Modell** sollte kein Passwort haben.

            modelsCreated(":", ":a")
        }

        when:
        executer.withArguments(ENABLE_CLI)
        def model2 = runBuildAction(new FetchCustomModelForEachProjectInTree())

        then:
        model2.size() == 2
        model2[0].message == "It works from project :"
        model2[1].message == "It works from project :a"

        and:
        fixture.assertStateLoaded()

        when:

## Modelos aninhados

Cada atributo de um modelo Pydantic tem um tipo.

Mas esse tipo pode ser outro modelo Pydantic.

Portanto, você pode declarar "objects" JSON profundamente aninhados com nomes, tipos e validações de atributos específicos.

Tudo isso, aninhado arbitrariamente.

### Defina um sub-modelo

Por exemplo, nós podemos definir um modelo `Image`:

```Python hl_lines="9-11"

# OpenAPI-`models`

OpenAPI Pydantic-Modelle, werden zum Generieren und Validieren der generierten OpenAPI verwendet.

# Extra Models

Continuing with the previous example, it will be common to have more than one related model.

This is especially the case for user models, because:

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

!!! danger
    Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.

Und es wird entsprechend annotiert/dokumentiert.

## Verschachtelte Modelle

Jedes Attribut eines Pydantic-Modells hat einen Typ.

Aber dieser Typ kann selbst ein anderes Pydantic-Modell sein.

Sie können also tief verschachtelte JSON-„Objekte“ deklarieren, mit spezifischen Attributnamen, -typen, und -validierungen.

## Nested Models

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel

For example, we can define an `Image` model:

=== "Python 3.10+"

        and:
        (model.left as GradleProject).name == "hello-world"
        (model.right as EclipseProject).gradleProject.name == "hello-world"

        and:
        def streamedModels = listener1.models as List<CustomModel>
        streamedModels.size() == 2
        streamedModels[0].value == 1
        streamedModels[1].value == 2


        when:
        withIsolatedProjects()

// Unpacks the given flatbuffer model.
//
// This helper is useful as UnPackTo requires the input to not have any existing
// state so directly calling UnPackTo could lead to memory leaks if the model
// already had some state. Instead, the returned object from here can be used to
// overwrite existing model.
ModelT UnPackFlatBufferModel(const Model& flatbuffer_model) {
  ModelT model;
  flatbuffer_model.UnPackTo(&model);