For example, you cannot put a Pydantic model in a `JSONResponse` without first converting it to a `dict` with all the data types (like `datetime`, `UUID`, etc) converted to JSON-compatible types.

For those cases, you can use the `jsonable_encoder` to convert your data before passing it to a response:

```Python hl_lines="6-7  21-22"

import java.util.List;
import java.util.Map;

import org.codelibs.core.beans.Converter;
import org.codelibs.core.beans.converter.DateConverter;
import org.codelibs.core.beans.converter.NumberConverter;
import org.codelibs.core.beans.converter.SqlDateConverter;
import org.codelibs.core.beans.converter.TimeConverter;
import org.codelibs.core.beans.converter.TimestampConverter;
import org.codelibs.core.convert.DateConversionUtil;

It would also mean that if you get data from the `Request` object directly (for example, read the body) it won't be validated, converted or documented (with OpenAPI, for the automatic API user interface) by FastAPI.

Although any other parameter declared normally (for example, the body with a Pydantic model) would still be validated, converted, annotated, etc.

But there are specific cases where it's useful to get the `Request` object.

Por ejemplo, no puedes poner un modelo Pydantic en una `JSONResponse` sin primero convertirlo a un `dict` con todos los tipos de datos (como `datetime`, `UUID`, etc) convertidos a tipos compatibles con JSON.

Para esos casos, puedes usar el `jsonable_encoder` para convertir tus datos antes de pasarlos a la respuesta:

```Python hl_lines="4 6 20 21"
{!../../docs_src/response_directly/tutorial001.py!}
```

/// note | Detalles Técnicos

Você também pode usar `dataclasses` no parâmetro `response_model`:

```Python hl_lines="1  7-13  19"
{!../../docs_src/dataclasses/tutorial002.py!}
```

A dataclass será automaticamente convertida para uma dataclass Pydantic.

Dessa forma, seu esquema aparecerá na interface de documentação da API:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses em Estruturas de Dados Aninhadas

Vous déclarez la forme de la donnée avec des classes et des attributs.

Chaque attribut possède un type.

Puis vous créez une instance de cette classe avec certaines valeurs et **Pydantic** validera les valeurs, les convertira dans le type adéquat (si c'est nécessaire et possible) et vous donnera un objet avec toute la donnée.

Ainsi, votre éditeur vous offrira un support adapté pour l'objet résultant.

      return INSTANCE;
    }

    private static final long serialVersionUID = 1;
  }

  /**
   * Returns a serializable converter object that converts between strings and shorts using {@link
   * Short#decode} and {@link Short#toString()}. The returned converter throws {@link
   * NumberFormatException} if the input string is invalid.
   *

      return INSTANCE;
    }

    private static final long serialVersionUID = 1;
  }

  /**
   * Returns a serializable converter object that converts between strings and shorts using {@link
   * Short#decode} and {@link Short#toString()}. The returned converter throws {@link
   * NumberFormatException} if the input string is invalid.
   *

* `frozenset`:
    * In requests and responses, treated the same as a `set`:
        * In requests, a list will be read, eliminating duplicates and converting it to a `set`.
        * In responses, the `set` will be converted to a `list`.
        * The generated schema will specify that the `set` values are unique (using JSON Schema's `uniqueItems`).
* `bytes`:
    * Standard Python `bytes`.

      return INSTANCE;
    }

    private static final long serialVersionUID = 1;
  }

  /**
   * Returns a serializable converter object that converts between strings and integers using {@link
   * Integer#decode} and {@link Integer#toString()}. The returned converter throws {@link
   * NumberFormatException} if the input string is invalid.
   *