As dependencies will also be called by **FastAPI** (the same as your *path operation functions*), the same rules apply while defining your functions.

You can use `async def` or normal `def`.

And you can declare dependencies with `async def` inside of normal `def` *path operation functions*, or `def` dependencies inside of `async def` *path operation functions*, etc.

It doesn't matter. **FastAPI** will know what to do.

!!! note

# Advanced Dependencies

## Parameterized dependencies

All the dependencies we have seen are a fixed function or class.

But there could be cases where you want to be able to set parameters on the dependency, without having to declare many different functions or classes.

Let's imagine that we want to have a dependency that checks if the query parameter `q` contains some fixed content.

But we want to be able to parameterize that fixed content.

        return {"fresh_value": fresh_value}
    ```

## 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
    All this might not seem as useful with these simple examples.

# Dependencies in path operation decorators

In some cases you don't really need the return value of a dependency inside your *path operation function*.

Or the dependency doesn't return a value.

But you still need it to be executed/solved.

For those cases, instead of declaring a *path operation function* parameter with `Depends`, you can add a `list` of `dependencies` to the *path operation decorator*.

!!! tip
    To declare File bodies, you need to use `File`, because otherwise the parameters would be interpreted as query parameters or body (JSON) parameters.

The files will be uploaded as "form data".

    * Como as estruturas de dados do Pydantic são apenas instâncias de classes que você define, a auto completação, _linting_, _mypy_ e a sua intuição devem funcionar corretamente com seus dados validados.
* Valida **estruturas complexas**:
    * Use modelos hierárquicos do Pydantic, `List` e `Dict` do `typing` do Python, etc.

const div = document.querySelector('.github-topic-projects')

async function getDataBatch(page) {
    const response = await fetch(`https://api.github.com/search/repositories?q=topic:fastapi&per_page=100&page=${page}`, { headers: { Accept: 'application/vnd.github.mercy-preview+json' } })
    const data = await response.json()
    return data
}

async function getData() {
    let page = 1
    let data = []
    let dataBatch = await getDataBatch(page)

    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
    hashed_password = hashed_password,
)
```

!!! warning
    The supporting additional functions are just to demo a possible flow of the data, but they of course are not providing any real security.

## Reduce duplication

Reducing code duplication is one of the core ideas in **FastAPI**.

sequenceDiagram

participant code as Code
participant function as say_hi()
participant execute as Funktion ausgeführt

    rect rgba(0, 255, 0, .1)
        code ->> function: say_hi(name="Camila")
        function ->> execute: führe Code der Funktion aus
        execute ->> code: gib das Resultat zurück
    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Camila")

            ws.onmessage = function(event) {
                var messages = document.getElementById('messages')
                var message = document.createElement('li')
                var content = document.createTextNode(event.data)
                message.appendChild(content)
                messages.appendChild(message)
            };
            function sendMessage(event) {