!!! info
    Para declarar cookies, necesitas usar `Cookie`, porque de lo contrario los parámetros serían interpretados como parámetros de query.

## Resumen

!!! tip
    The next sections are **not necessarily "advanced"**.

    And it's possible that for your use case, the solution is in one of them.

## Read the Tutorial first

The next sections assume you already read the main [Tutorial - User Guide: Security](../../tutorial/security/index.md){.internal-link target=_blank}.

* La documentación alternativa también reflejará el nuevo parámetro de query y el body:

![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)

### Resumen

En resumen, declaras los tipos de parámetros, body, etc. **una vez** como parámetros de la función.

Lo haces con tipos modernos estándar de Python.

[build-system]
requires = ["pdm-backend"]
build-backend = "pdm.backend"

[project]
name = "fastapi"
dynamic = ["version"]
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
readme = "README.md"
requires-python = ">=3.8"
authors = [
    { name = "Sebastián Ramírez", email = "******@****.***" },
]
classifiers = [
    "Intended Audience :: Information Technology",

Pero toda esta funcionalidad de usar código asincrónico con `async` y `await` se resume muchas veces como usar "coroutines". Es comparable a la característica principal de Go, las "Goroutines".

## Conclusión

Veamos la misma frase de arriba:

Vous aurez de nombreux, nombreux utilisateurs, mais votre serveur attendra 🕙 que leur connexion peu performante envoie des requêtes.

Puis vous attendrez 🕙 de nouveau que leurs réponses reviennent.

Cette "attente" 🕙 se mesure en microsecondes, mais tout de même, en cumulé cela fait beaucoup d'attente.

C'est pourquoi il est logique d'utiliser du code asynchrone ⏸🔀⏯ pour des APIs web.

    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):
    result = ml_models["answer_to_everything"](x)
    return {"result": result}
```

**Note**: This is the recommended way going forward, instead of using `startup` and `shutdown` events.

That way I could find the best ways to reduce code duplication as much as possible, to have completion everywhere, type and error checks, etc.

All in a way that provided the best development experience for all the developers.

## Requirements

* **Fast to code**: Increase the speed to develop features by about 200% to 300%. *
* **Fewer bugs**: Reduce about 40% of human (developer) induced errors. *
* **Intuitive**: Great editor support. <abbr title="also known as auto-complete, autocompletion, IntelliSense">Completion</abbr> everywhere. Less time debugging.

So, continuing with the `user_dict` from above, writing:

```Python
UserInDB(**user_dict)
```

would result in something equivalent to:

```Python
UserInDB(
    username="john",
    password="secret",
    email="******@****.***",
    full_name=None,
)
```