buffer.writeUtf8CodePoint(0x1a)
    buffer.writeUtf8CodePoint(0x0a)
    server.enqueue(
      MockResponse
        .Builder()
        .body(buffer)
        .setHeader("Content-Type", "image/png; charset=utf-8")
        .build(),
    )
    val response = client.newCall(request().build()).execute()
    response.body.close()
    applicationLogs
      .assertLogEqual("--> GET $url")

///

Die interaktive API-Dokumentation wird entsprechend aktualisiert und erlaubt jetzt mehrere Werte.

<img src="/img/tutorial/query-params-str-validations/image02.png">

### Query-Parameter-Liste / Mehrere Werte mit Defaults

Und Sie können auch eine Default-`list`e von Werten definieren, wenn keine übergeben werden:

///

## 🛠️ ⏮️ 🗄

🌐 📨 📄, 🔬 & 📄 👆 🔗 (& 🎧-🔗) 🔜 🛠️ 🎏 🗄 🔗.

, 🎓 🩺 🔜 ✔️ 🌐 ℹ ⚪️➡️ 👫 🔗 💁‍♂️:

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

## 🙅 ⚙️

🚥 👆 👀 ⚫️, *➡ 🛠️ 🔢* 📣 ⚙️ 🕐❔ *➡* &amp; *🛠️* 🏏, &amp; ⤴️ **FastAPI** ✊ 💅 🤙 🔢 ⏮️ ☑ 🔢, ❎ 📊 ⚪️➡️ 📨.

🤙, 🌐 (⚖️ 🏆) 🕸 🛠️ 👷 👉 🎏 🌌.

If you open the automatic API docs, your extension will show up at the bottom of the specific *path operation*.

<img src="/img/tutorial/path-operation-advanced-configuration/image01.png">

And if you see the resulting OpenAPI (at `/openapi.json` in your API), you will see your extension as part of the specific *path operation* too:

```JSON hl_lines="22"
{
    "openapi": "3.1.0",
    "info": {

have different security properties and provide different levels of confidence
when dealing with untrusted data. Based on the security history of these
libraries we consider that it is safe to work with untrusted inputs for PNG,
BMP, GIF, WAV, RAW, RAW\_PADDED, CSV and PROTO formats. All other input formats,
including tensorflow-io should be sandboxed if used to process untrusted data.

  - navigation.tracking
  - search.highlight
  - search.share
  - search.suggest
  - toc.follow
  icon:
    repo: fontawesome/brands/github-alt
  logo: img/icon-white.svg
  favicon: img/favicon.png
  language: en
repo_name: fastapi/fastapi
repo_url: https://github.com/fastapi/fastapi
plugins:
  search: null
  macros:
    include_yaml:
    - external_links: ../en/data/external_links.yml

## Soporte de editor en todas partes

Y obtienes soporte de editor en todas partes.

Incluso para elementos dentro de listas:

<img src="/img/tutorial/body-nested-models/image01.png">

No podrías obtener este tipo de soporte de editor si estuvieras trabajando directamente con `dict` en lugar de modelos Pydantic.

///

La documentación interactiva de API se actualizará en consecuencia, para permitir múltiples valores:

<img src="/img/tutorial/query-params-str-validations/image02.png">

### Lista de parámetros de Query / múltiples valores con valores por defecto

Y también puedes definir un valor por defecto `list` de valores si no se proporcionan ninguno:

![Interceptors Diagram](../assets/images/interceptors@2x.png)

### Application Interceptors

Interceptors are registered as either _application_ or _network_ interceptors. We'll use the `LoggingInterceptor` defined above to show the difference.

<div class="screenshot">
<img src="/img/tutorial/sql-databases/image01.png">
</div>

## Update the App with Multiple Models { #update-the-app-with-multiple-models }

Now let's **refactor** this app a bit to increase **security** and **versatility**.