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>

@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();

    /**

///

/// 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.

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 }

///

## Separating tests { #separating-tests }

In a real application, you probably would have your tests in a different file.

And your **FastAPI** application might also be composed of several files/modules, etc.

### **FastAPI** app file { #fastapi-app-file }

Let's say you have a file structure as described in [Bigger Applications](bigger-applications.md){.internal-link target=_blank}:

```
.
├── app

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

/// note | Remarque

`JSONResponse` est elle-même une sous-classe de `Response`.

///

Et quand vous retournez une `Response`, **FastAPI** la transmet directement.

Elle ne fera aucune conversion de données avec les modèles Pydantic, elle ne convertira pas le contenu en un type quelconque.

Cela vous donne beaucoup de flexibilité. Vous pouvez retourner n'importe quel type de données, surcharger n'importe quelle déclaration ou validation de données.