* Alle Schlüsselwort-Argumente, die an die Taskfunktion übergeben werden sollen (`message="some notification"`).

## Dependency Injection { #dependency-injection }

* Any keyword arguments that should be passed to the task function (`message="some notification"`).

## Dependency Injection { #dependency-injection }

Using `BackgroundTasks` also works with the dependency injection system, you can declare a parameter of type `BackgroundTasks` at multiple levels: in a *path operation function*, in a dependency (dependable), in a sub-dependency, etc.

* Любые именованные аргументы, которые должны быть переданы функции задачи (`message="some notification"`).

## Встраивание зависимостей { #dependency-injection }

* das Handbuch für fortgeschrittene Benutzer
* die SQLModel-Dokumentation
* die API-Dokumentation
* die automatische Dokumentation

* Data Science
* Deep Learning
* Machine Learning
* Dependency Injection
* HTTP Basic-Authentifizierung
* HTTP Digest
* ISO-Format
* der JSON-Schema-Standard
* das JSON-Schema
* die Schema-Definition
* Password Flow
* Mobile

* deprecatet
* designt

async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):
    return {"fresh_value": fresh_value}
```

////

## Recap { #recap }

Apart from all the fancy words used here, the **Dependency Injection** system is quite simple.

Just functions that look the same as the *path operation functions*.

But still, it is very powerful, and allows you to declare arbitrarily deeply nested dependency "graphs" (trees).

/// tip

    return {"fresh_value": fresh_value}
```

////

## Zusammenfassung { #recap }

Abgesehen von all den ausgefallenen Wörtern, die hier verwendet werden, ist das **Dependency Injection**-System recht simpel.

Einfach Funktionen, die genauso aussehen wie *Pfadoperation-Funktionen*.

Dennoch ist es sehr mächtig und ermöglicht Ihnen die Deklaration beliebig tief verschachtelter Abhängigkeits-„Graphen“ (Bäume).

# Classes as Dependencies { #classes-as-dependencies }

Before diving deeper into the **Dependency Injection** system, let's upgrade the previous example.

## A `dict` from the previous example { #a-dict-from-the-previous-example }

In the previous example, we were returning a `dict` from our dependency ("dependable"):

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[9] *}

///

## Otros modelos { #other-models }

Ahora puedes obtener el usuario actual directamente en las *path operation functions* y manejar los mecanismos de seguridad a nivel de **Dependency Injection**, usando `Depends`.

Y puedes usar cualquier modelo o datos para los requisitos de seguridad (en este caso, un modelo de Pydantic `User`).

Pero no estás limitado a usar algún modelo de datos, clase o tipo específico.

* the Tutorial - User guide
* the Advanced User Guide
* the SQLModel docs
* the API docs
* the automatic docs

* Data Science
* Deep Learning
* Machine Learning
* Dependency Injection
* HTTP Basic authentication
* HTTP Digest
* ISO format
* the JSON Schema standard
* the JSON schema
* the schema definition
* Password Flow
* Mobile

* deprecated
* designed
* invalid

* Quaisquer argumentos nomeados que devem ser passados para a função de tarefa (`message="some notification"`).

## Injeção de dependências { #dependency-injection }