*   Keras compile/fit behavior for functional and subclassed models have been
    unified. Model properties such as `metrics`, `metrics_names` will now be
    available only after **training/evaluating the model on actual data** for
    functional models. `metrics` will **now include** model `loss` and output
    losses.`loss_functions` property has been removed from the model. This was

* 🌐 Add Korean translation for `docs/ko/docs/tutorial/header-param-models.md`. PR [#13001](https://github.com/fastapi/fastapi/pull/13001) by [@hard-coders](https://github.com/hard-coders).

* Recuperar los datos almacenados.
* Poner esos datos en un modelo de Pydantic.
* Generar un `dict` sin valores por defecto del modelo de entrada (usando `exclude_unset`).
    * De esta manera puedes actualizar solo los valores realmente establecidos por el usuario, en lugar de sobrescribir valores ya almacenados con valores por defecto en tu modelo.

* Fügen Sie diese in ein Pydantic-Modell ein.
* Erzeugen Sie aus dem empfangenen Modell ein `dict` ohne Defaultwerte (mittels `exclude_unset`).
    * So ersetzen Sie nur die tatsächlich vom Benutzer gesetzten Werte, statt dass bereits gespeicherte Werte mit Defaultwerten des Modells überschrieben werden.

You can declare examples of the data your app can receive.

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models { #extra-json-schema-data-in-pydantic-models }

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

//// tab | Pydantic v2

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

////

 * to help identify where specific model elements came from.
 *
 * @since 4.0.0
 */
public class InputSource implements Serializable {

    private final String modelId;
    private final String location;
    private final List<InputSource> inputs;
    private final InputLocation importedFrom;

    public InputSource(String modelId, String location) {

* Recuperar os dados armazenados.
* Colocar esses dados em um modelo do Pydantic.
* Gerar um `dict` sem valores padrão a partir do modelo de entrada (usando `exclude_unset`).
    * Dessa forma, você pode atualizar apenas os valores definidos pelo usuário, em vez de substituir os valores já armazenados com valores padrão em seu modelo.

Using this same trick, you could use a Pydantic model to define the JSON Schema that is then included in the custom OpenAPI schema section for the *path operation*.

And you could do this even if the data type in the request is not JSON.

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

/// info | Informação

Para uma explicação mais completa de `**user_dict`, verifique [a documentação para **Extra Models**](../extra-models.md#about-user_indict){.internal-link target=_blank}.

///

## Retorne o token

A resposta do endpoint `token` deve ser um objeto JSON.

      </plugin>
      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <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>