<img src="/img/python-types/image06.png">

Notice that this means "`one_person` is an **instance** of the class `Person`".

It doesn't mean "`one_person` is the **class** called `Person`".

## Pydantic models { #pydantic-models }

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

# Código de Estado del Response

De la misma manera que puedes especificar un modelo de response, también puedes declarar el código de estado HTTP usado para el response con el parámetro `status_code` en cualquiera de las *path operations*:

* `@app.get()`
* `@app.post()`
* `@app.put()`
* `@app.delete()`
* etc.

{* ../../docs_src/response_status_code/tutorial001.py hl[6] *}

/// note | Nota

qui sont relativement fortement couplées.

/// check | A inspiré **FastAPI** à

Définir des validations supplémentaires pour les types de données utilisant la valeur "par défaut" des attributs du modèle. Ceci améliore le support de l'éditeur, et n'était pas disponible dans Pydantic auparavant.

 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package model

import java.util.LinkedList

/**
 * Split a list of elements into nearly even sublist. If an element is too large, largeElementSplitFunction will be used to split the large element into several smaller pieces;

And then you can set headers in that *temporal* response object.

{* ../../docs_src/response_headers/tutorial002.py hl[1, 7:8] *}

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

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.

## Use the `Request` object directly { #use-the-request-object-directly }

from pydantic import BaseModel

# Deklarieren Sie eine Variable als ein `str`
# und bekommen Sie Editor-Unterstütung innerhalb der Funktion
def main(user_id: str):
    return user_id


# Ein Pydantic-Modell
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Das kann nun wie folgt verwendet werden:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

      <item index="4" class="java.lang.String" itemvalue="org.gradle.internal.scan.UsedByScanPlugin" />
      <item index="5" class="java.lang.String" itemvalue="org.gradle.api.model.ManagedObjectCreator" />
    </list>
    <pattern value="org.gradle.internal.service.ServiceRegistrationProvider" hierarchically="true" method="configure" />
  </component>

 ╭────────── FastAPI CLI - Development mode ───────────╮
 │                                                     │
 │  Serving at: http://127.0.0.1:8000                  │
 │                                                     │
 │  API docs: http://127.0.0.1:8000/docs               │
 │                                                     │
 │  Running in development mode, for production use:   │

```Python
from datetime import date

from pydantic import BaseModel

# Declareer een variabele als een str
# en krijg editorondersteuning in de functie
def main(user_id: str):
    return user_id


# Een Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Vervolgens kan je het op deze manier gebruiken:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")