Stellen wir uns vor, dass das Laden des Modells **eine ganze Weile dauern** kann, da viele **Daten von der Festplatte** gelesen werden müssen. Sie möchten das also nicht für jeden Request tun.

     */
    private static class MockModelBuilderResult implements ModelBuilderResult {
        private final Model effectiveModel;
        private final Model rawModel;
        private final Model fileModel;

        MockModelBuilderResult(Model effectiveModel, Model rawModel, Model fileModel) {
            this.effectiveModel = effectiveModel;
            this.rawModel = rawModel;

## Import `Field` { #import-field }

First, you have to import it:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[4] *}


/// warning

Notice that `Field` is imported directly from `pydantic`, not from `fastapi` as are all the rest (`Query`, `Path`, `Body`, etc).

///

## Declare model attributes { #declare-model-attributes }

Sebastián Ramírez <******@****.***> 1731896744 +0100

    hashed_password = user_dict["hashed_password"],
)
```

/// info | Información

Para una explicación más completa de `**user_dict` revisa en [la documentación para **Extra Models**](../extra-models.md#about-user-in-dict){.internal-link target=_blank}.

///

## Devolver el token { #return-the-token }

El response del endpoint `token` debe ser un objeto JSON.

Você pode declarar exemplos dos dados que sua aplicação pode receber.

Aqui estão várias maneiras de fazer isso.

## Dados extras de JSON Schema em modelos Pydantic { #extra-json-schema-data-in-pydantic-models }

Você pode declarar `examples` para um modelo Pydantic que serão adicionados ao JSON Schema gerado.

//// tab | Pydantic v2

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

////

Puedes declarar ejemplos de los datos que tu aplicación puede recibir.

Aquí tienes varias formas de hacerlo.

## Datos extra de JSON Schema en modelos de Pydantic { #extra-json-schema-data-in-pydantic-models }

Puedes declarar `examples` para un modelo de Pydantic que se añadirá al JSON Schema generado.

//// tab | Pydantic v2

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

////

Sebastián Ramírez <******@****.***> 1731896744 +0100

timothy <******@****.***> 1748612313 +0900

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted