///

Este parámetro no crea ese endpoint / *path operation*, pero declara que la URL `/token` será la que el cliente deberá usar para obtener el token. Esa información se usa en OpenAPI, y luego en los sistemas de documentación interactiva del API.

Pronto también crearemos la verdadera *path operation*.

🚥 👆 💪 2️⃣ ⏲ ↗️ ❔ ⚙️ 🐍 🆎 (🚥 👆 🚫 ⚙️ FastAPI), ✅ 📏 🔰: [🐍 🆎](python-types.md){.internal-link target=_blank}.

👆 ✍ 🐩 🐍 ⏮️ 🆎:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


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

Escribes Python estándar con tipos:

```Python
from datetime import date

from pydantic import BaseModel

# Declara una variable como un str
# y obtiene soporte del editor dentro de la función
def main(user_id: str):
    return user_id


# Un modelo de Pydantic
class User(BaseModel):
    id: int
    name: str

But if you return a `Response` directly (or any subclass, like `JSONResponse`), the data won't be automatically converted (even if you declare a `response_model`), and the documentation won't be automatically generated (for example, including the specific "media type", in the HTTP header `Content-Type` as part of the generated OpenAPI).

If you go to `/docs`, you will see that it has the **schemas** for the data to be sent in requests and received in responses:

<img src="/img/tutorial/generate-clients/image01.png">

You can see those schemas because they were declared with the models in the app.

That information is available in the app's **OpenAPI schema**, and then shown in the API docs.

Vous écrivez du python standard avec des annotations de types:

```Python
from datetime import date

from pydantic import BaseModel

# Déclare une variable comme étant une str
# et profitez de l'aide de votre IDE dans cette fonction
def main(user_id: str):
    return user_id


# Un modèle Pydantic
class User(BaseModel):
    id: int
    name: str

///

### Crear el objeto `Settings`

Importa `BaseSettings` de Pydantic y crea una sub-clase, muy similar a un modelo de Pydantic.

De la misma forma que con los modelos de Pydantic, declaras atributos de clase con anotaciones de tipos, y posiblemente, valores por defecto.

Next it will convert and validate the data. So, when you use that `settings` object, you will have data of the types you declared (e.g. `items_per_user` will be an `int`).

### Use the `settings` { #use-the-settings }

Then you can use the new `settings` object in your application:

{* ../../docs_src/settings/tutorial001.py hl[18:20] *}

شما پایتون استاندارد را با استفاده از تایپ ها مینویسید:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


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

      <artifactId>guava</artifactId>
      <version>${project.version}</version>
    </dependency>
    <!-- We redeclare the j2objc-annotations dependency from `guava`: Our Gradle
         Module Metadata hides the dependency declared in `guava` from runtime
         configurations downstream, and GWT uses the runtime configuration for
         its builds. Thus, GWT doesn't get j2objc-annotations transitively, in