## Универсальная поддержка редактора

И вы получаете поддержку редактора везде.

Даже для элементов внутри списков:

<img src="/img/tutorial/body-nested-models/image01.png">

Вы не могли бы получить такую поддержку редактора, если бы работали напрямую с `dict`, а не с моделями Pydantic.

    * Composite bodies' IDs are now based on path, not only on route name, as the auto-generated name uses the function names, that can be duplicated in different modules.
    * The same new ID generation applies to response models.
    * This also changes the generated title for those models.

		if modDir != "" {
			// In module mode, the user may reasonably expect the entire module
			// to be present. Copy it over.
			deviceModDir := path.Join(deviceGopath, "src", modPath)
			if err := adb("exec-out", "mkdir", "-p", path.Dir(deviceModDir)); err != nil {
				return 0, err
			}
			// We use a single recursive 'adb push' of the module root instead of

        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.0.0</version>
          <models>
            <model>src/main/mdo/profiles.mdo</model>
            <model>src/main/mdo/paramdoc.mdo</model>
          </models>
        </configuration>
        <executions>
          <execution>
            <id>modello</id>
            <goals>

///

/// tip | Dica



///

	Se você está se perguntando, "AlexNet", "ResNet", e "LeNet" são apenas nomes de <abbr title="técnicamente, modelos de arquitetura de Deep Learning">modelos</abbr> de Machine Learning (aprendizado de máquina).

### Declare um *parâmetro de rota*

Logo, crie um *parâmetro de rota* com anotações de tipo usando a classe enum que você criou (`ModelName`):

///

### Pydantic- und FastAPI-`examples`

Wenn Sie `examples` innerhalb eines Pydantic-Modells hinzufügen, indem Sie `schema_extra` oder `Field(examples=["something"])` verwenden, wird dieses Beispiel dem **JSON-Schema** für dieses Pydantic-Modell hinzugefügt.

Und dieses **JSON-Schema** des Pydantic-Modells ist in der **OpenAPI** Ihrer API enthalten und wird dann in der Benutzeroberfläche der Dokumentation verwendet.

/// tip | Consejo

`JSONResponse` en sí misma es una subclase de `Response`.

///

Y cuando devuelves una `Response`, **FastAPI** la pasará directamente.

No hará ninguna conversión de datos con los modelos de Pydantic, no convertirá los contenidos a ningún tipo, etc.

Esto te da mucha flexibilidad. Puedes devolver cualquier tipo de datos, sobrescribir cualquier declaración o validación de datos, etc.

@Immutable
public interface DependencyCoordinates extends ArtifactCoordinates {
    /**
     * {@return the type of the dependency}
     * A dependency can be a <abbr>JAR</abbr> file,
     * a modular-<abbr>JAR</abbr> if it is intended to be placed on the module path,
     * a <abbr>JAR</abbr> containing test classes, a POM file, <i>etc.</i>
     */
    @Nonnull
    Type getType();

    /**

Remember that when you import `Query`, `Path`, and others from `fastapi`, those are actually functions that return special classes.

///

/// tip

Notice how each model's attribute with a type, default value and `Field` has the same structure as a *path operation function's* parameter, with `Field` instead of `Path`, `Query` and `Body`.

///

## Add extra information { #add-extra-information }

computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted
models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions