import pytest
from dirty_equals import IsDict
from fastapi.testclient import TestClient

from ...utils import needs_py39


@pytest.fixture(name="client")
def get_client():
    from docs_src.bigger_applications.app_an_py39.main import app

    client = TestClient(app)
    return client


@needs_py39
def test_users_token_jessica(client: TestClient):
    response = client.get("/users?token=jessica")

👆 ✍ 🐩 🐍 ⏮️ 🆎:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

👈 💪 ⤴️ ⚙️ 💖:

```Python

```Python hl_lines="16-18  21-22  28-32"
{!../../docs_src/openapi_callbacks/tutorial001.py!}
```

There are 2 main differences from a normal *path operation*:

* It doesn't need to have any actual code, because your app will never call this code. It's only used to document the *external API*. So, the function could just have `pass`.

string.
Ceci est particulièrement utile pour les données entrantes.

Sans un système de validation des données, vous devriez effectuer toutes les vérifications à la main, dans le code.

Ces fonctionnalités sont ce pourquoi Marshmallow a été construit. C'est une excellente bibliothèque, et je l'ai déjà beaucoup utilisée.

<%@page pageEncoding="UTF-8" contentType="text/html; charset=UTF-8" %>
<aside class="main-sidebar sidebar-dark-primary">
	<la:link href="/admin/" styleClass="brand-link text-center">
		<span class="brand-text">
		<c:if test="${empty param.brandName or empty param.logoPath}"><img src="${fe:url('/images/logo-head.png')}" alt="<la:message key="labels.header_brand_name" />" /></c:if

 -----
 Introduction
 -----
 Hervé Boutemy
 -----
 2013-03-19
 -----

Maven Model Builder

 The effective model builder, with profile activation, inheritance, interpolation, ...

 The main component is <<<ModelBuilder>>>
 ({{{./apidocs/org/apache/maven/model/building/ModelBuilder.html}javadoc}},
 {{{./xref/org/apache/maven/model/building/ModelBuilder.html}source}}),
 with its <<<DefaultModelBuilder>>> implementation

Você escreve Python padrão com tipos:

```Python
from datetime import date

from pydantic import BaseModel

# Declare uma variável como str
# e obtenha suporte do editor dentro da função
def main(user_id: str):
    return user_id


# Um modelo do Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Que então pode ser usado como:

```Python

researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)

            final SearchRequestParams params, final OptionalThing<FessUserBean> userBean) {
        if (logger.isDebugEnabled()) {
            logger.debug("Send {} to the main searcher.", query);
        }
        final int pageSize = params.getPageSize();
        final SearchResult searchResult = searcher.search(query, params, userBean);

    }

    @Test
    public void testLoadSave() throws IOException {
        StringWriter sw = new StringWriter();
        PrintWriter pw = new PrintWriter(sw);
        pw.println("# ");
        pw.println("# The Main  ");
        pw.println("# ");
        pw.println("# Comment ");
        pw.println("# ");
        pw.println("");
        pw.println("# Another comment");
        pw.println("");