This way, its schema will show up in the API docs user interface:

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

## Dataclasses in Nested Data Structures

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

* `int`
* `float`
* `bool`
* `bytes`

```Python hl_lines="1"
{!../../../docs_src/python_types/tutorial005.py!}
```

### Types génériques avec des paramètres de types

Il existe certaines structures de données qui contiennent d'autres valeurs, comme `dict`, `list`, `set` et `tuple`. Et les valeurs internes peuvent elles aussi avoir leurs propres types.

    * 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**:
    * Utilise les modèles hiérarchique de Pydantic, le `typage` Python pour les `Lists`, `Dict`, etc.

### Project file structure

Let's say your project file structure looks like this:

```
.
├── app
│   ├── __init__.py
│   ├── main.py
```

Now create a directory to store those static files.

Your new file structure could look like this:

```
.
├── app
│   ├── __init__.py
│   ├── main.py
└── static/
```

# Codes HTTP supplémentaires

Par défaut, **FastAPI** renverra les réponses à l'aide d'une structure de données `JSONResponse`, en plaçant la réponse de votre  *chemin d'accès* à l'intérieur de cette `JSONResponse`.

Il utilisera le code HTTP par défaut ou celui que vous avez défini dans votre *chemin d'accès*.

## Codes HTTP supplémentaires

# Tutorial - User Guide

This tutorial shows you how to use **FastAPI** with most of its features, step by step.

Each section gradually builds on the previous ones, but it's structured to separate topics, so that you can go directly to any specific one to solve your specific API needs.

It is also built to work as a future reference.

So you can come back and see exactly what you need.

## Run the code

    ```Python hl_lines="3"
    {!> ../../../docs_src/header_params/tutorial001.py!}
    ```

## Declare `Header` parameters

Then declare the header parameters using the same structure as with `Path`, `Query` and `Cookie`.

The first value is the default value, you can pass all the extra validation or annotation parameters:

=== "Python 3.10+"

    ```Python hl_lines="9"

In a similar way you could use any other ORM.

!!! tip
    There's an equivalent article using Peewee here in the docs.

## File structure

For these examples, let's say you have a directory named `my_super_project` that contains a sub-directory called `sql_app` with a structure like this:

```
.
└── sql_app
    ├── __init__.py
    ├── crud.py
    ├── database.py
    ├── main.py
    ├── models.py

* `int`
* `float`
* `bool`
* `bytes`

```Python hl_lines="1"
{!../../../docs_src/python_types/tutorial005.py!}
```

### Generic types with type parameters

There are some data structures that can contain other values, like `dict`, `list`, `set` and `tuple`. And the internal values can have their own type too.