- tutorial/path-operation-configuration.md
    - tutorial/encoder.md
    - tutorial/body-updates.md
    - Dependencies:
      - tutorial/dependencies/index.md
      - tutorial/dependencies/classes-as-dependencies.md
      - tutorial/dependencies/sub-dependencies.md
      - tutorial/dependencies/dependencies-in-path-operation-decorators.md
      - tutorial/dependencies/global-dependencies.md

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.

    * Parce que les structures de données de pydantic consistent seulement en une instance de classe que vous définissez; l'auto-complétion, le linting, mypy et votre intuition devrait être largement suffisante pour valider vos données.
* Valide les **structures complexes**:

Resumindo, você declara **uma vez** os tipos dos parâmetros, corpo etc. como parâmetros de função.

Você faz com tipos padrão do Python moderno.

Você não terá que aprender uma nova sintaxe, métodos ou classes de uma biblioteca específica etc.

Apenas **Python** padrão.

Por exemplo, para um `int`:

```Python
item_id: int
```

ou para um modelo mais complexo, `Item`:

```Python

# Custom Response Classes - File, HTML, Redirect, Streaming, etc.

There are several custom response classes you can use to create an instance and return them directly from your *path operations*.

Read more about it in the [FastAPI docs for Custom Response - HTML, Stream, File, others](https://fastapi.tiangolo.com/advanced/custom-response/).

You can import them directly from `fastapi.responses`:

```python
from fastapi.responses import (
    FileResponse,

# Classes as Dependencies

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

## A `dict` from the previous example

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

=== "Python 3.10+"

    ```Python hl_lines="9"
    {!> ../../../docs_src/dependencies/tutorial001_an_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="11"

Vous faites cela avec les types Python standard modernes.

Vous n'avez pas à apprendre une nouvelle syntaxe, les méthodes ou les classes d'une bibliothèque spécifique, etc.

Juste du **Python** standard.

Par exemple, pour un `int`:

```Python
item_id: int
```

ou pour un modèle `Item` plus complexe :

```Python

### Recap

In summary, you declare **once** the types of parameters, body, etc. as function parameters.

You do that with standard modern Python types.

You don't have to learn a new syntax, the methods or classes of a specific library, etc.

Just standard **Python**.

For example, for an `int`:

```Python
item_id: int
```

or for a more complex `Item` model:

```Python
item: Item
```

## Renvoyer une `Response`

En fait, vous pouvez retourner n'importe quelle `Response` ou n'importe quelle sous-classe de celle-ci.

!!! note "Remarque"
    `JSONResponse` est elle-même une sous-classe de `Response`.

Et quand vous retournez une `Response`, **FastAPI** la transmet directement.

    SQLAlchemy uses the term "**model**" to refer to these classes and instances that interact with the database.

    But Pydantic also uses the term "**model**" to refer to something different, the data validation, conversion, and documentation classes and instances.

Import `Base` from `database` (the file `database.py` from above).

Create classes that inherit from it.

These classes are the SQLAlchemy models.