auto_error: Annotated[
            bool,
            Doc(
                """
                By default, if the HTTP Basic authentication is not provided (a
                header), `HTTPBasic` will automatically cancel the request and send the
                client an error.

                If `auto_error` is set to `False`, when the HTTP Basic authentication

            bool,
            Doc(
                """
                By default, if no HTTP Authorization header is provided, required for
                OAuth2 authentication, it will automatically cancel the request and
                send the client an error.

                If `auto_error` is set to `False`, when the HTTP Authorization header

        ] = None,
        auto_error: Annotated[
            bool,
            Doc(
                """
                By default, if the query parameter is not provided, `APIKeyQuery` will
                automatically cancel the request and send the client an error.

                If `auto_error` is set to `False`, when the query parameter is not
                available, instead of erroring out, the dependency result will be

            bool,
            Doc(
                """
                By default, if no HTTP Authorization header is provided, required for
                OpenID Connect authentication, it will automatically cancel the request
                and send the client an error.

                If `auto_error` is set to `False`, when the HTTP Authorization header

</blockquote>

## Recherche

En utilisant toutes les alternatives précédentes, j'ai eu la chance d'apprendre de toutes, de prendre des idées, et de les combiner de la meilleure façon que j'ai pu trouver pour moi-même et les équipes de développeurs avec lesquelles j'ai travaillé.

* L'apprentissage profond (ou **Deep Learning**) : est un sous-domaine du **Machine Learning**, donc les mêmes raisons s'appliquent. Avec la différence qu'il n'y a pas une unique feuille de calcul de nombres à multiplier, mais une énorme quantité d'entre elles, et dans de nombreux cas, on utilise un processeur...

* In many cases, it's better to understand their **underlying problem or use case**, because there might be a better way to solve it than what they are trying to do.

### Ask to close

If they reply, there's a high chance you would have solved their problem, congrats, **you're a hero**! 🦸

* Now, if that solved their problem, you can ask them to:

    * In GitHub Discussions: mark the comment as the **answer**.

</blockquote>

## Investigation

By using all the previous alternatives I had the chance to learn from all of them, take ideas, and combine them in the best way I could find for myself and the teams of developers I have worked with.

For example, it was clear that ideally it should be based on standard Python type hints.

</blockquote>

## Investigação

Ao usar todas as alternativas anteriores, eu tive a chance de aprender com todas elas, aproveitar ideias e combiná-las da melhor maneira que encontrei para mim e para os times de desenvolvedores com os quais trabalhava.

Previous versions of FastAPI (before <abbr title="before 2023-03">0.95.0</abbr>) required you to use `Query` as the default value of your parameter, instead of putting it in `Annotated`, there's a high chance that you will see code using it around, so I'll explain it to you.

!!! tip
    For new code and whenever possible, use `Annotated` as explained above. There are multiple advantages (explained below) and no disadvantages. 🍰