def get_flat_dependant(
    dependant: Dependant,
    *,
    skip_repeats: bool = False,
    visited: Optional[List[CacheKey]] = None,
) -> Dependant:
    if visited is None:
        visited = []
    visited.append(dependant.cache_key)

    flat_dependant = Dependant(
        path_params=dependant.path_params.copy(),

---

Common examples of CPU bound operations are things that require complex math processing.

For example:

* **Audio** or **image processing**.
* **Computer vision**: an image is composed of millions of pixels, each pixel has 3 values / colors, processing that normally requires computing something on those pixels, all at the same time.

you run **a single process per container** you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).

And then you can set those same memory limits and requirements in your configurations for your container management system (for example in **Kubernetes**). That way it will be able to **replicate the containers** in the **available machines** taking into account the amount of memory needed by...

---

Typische Beispiele für CPU-lastige Vorgänge sind Dinge, die komplexe mathematische Berechnungen erfordern.

Zum Beispiel:

* **Audio-** oder **Bildbearbeitung**.
* **Computer Vision**: Ein Bild besteht aus Millionen von Pixeln, jedes Pixel hat 3 Werte / Farben, die Verarbeitung erfordert normalerweise, Berechnungen mit diesen Pixeln durchzuführen, alles zur gleichen Zeit.

---

Ejemplos típicos de operaciones dependientes de CPU son cosas que requieren un procesamiento matemático complejo.

Por ejemplo:

* **Audio** o **procesamiento de imágenes**.
* **Visión por computadora**: una imagen está compuesta de millones de píxeles, cada píxel tiene 3 valores / colores, procesamiento que normalmente requiere calcular algo en esos píxeles, todo al mismo tiempo.

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: FastAPI
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Sebastián
    family-names: Ramírez
    email: ******@****.***
identifiers:
repository-code: 'https://github.com/tiangolo/fastapi'

    ```

This will make `tags` be a list, although it doesn't declare the type of the elements of the list.

## List fields with type parameter

But Python has a specific way to declare lists with internal types, or "type parameters":

### Import typing's `List`

In Python 3.9 and above you can use the standard `list` to declare these type annotations as we'll see below. 💡

## About HTTP status codes

!!! note
    If you already know what HTTP status codes are, skip to the next section.

In HTTP, you send a numeric status code of 3 digits as part of the response.

These status codes have a name associated to recognize them, but the important part is the number.

In short:

## List of models

The same way, you can declare responses of lists of objects.

For that, use the standard Python `typing.List` (or just `list` in Python 3.9 and above):

=== "Python 3.9+"

    ```Python hl_lines="18"
    {!> ../../../docs_src/extra_models/tutorial004_py39.py!}

* Valide les **structures complexes**:
    * Utilise les modèles hiérarchique de Pydantic, le `typage` Python pour les `Lists`, `Dict`, etc.
    * Et les validateurs permettent aux schémas de données complexes d'être clairement et facilement définis, validés et documentés sous forme d'un schéma JSON.