}

        final TimeoutTask timeoutTask = createTimeoutTask();
        try {
            executePython();
        } catch (final Exception e) {
            logger.warn("Failed to run python command.", e);
            resultBuf.append(e.getMessage()).append("\n");
        } finally {
            if (timeoutTask != null && !timeoutTask.isCanceled()) {
                timeoutTask.cancel();
            }

### Desativar a documentação automática

O primeiro passo é desativar a documentação automática, pois por padrão, ela usa o CDN padrão.

Para desativá-los, defina suas URLs como `None` ao criar seu aplicativo `FastAPI`:

```Python hl_lines="8"
{!../../docs_src/custom_docs_ui/tutorial001.py!}
```

### Incluir a documentação personalizada

Agora você pode criar as *operações de rota* para a documentação personalizada.

    * Com <a href="https://tartiflette.github.io/tartiflette-asgi/" class="external-link" target="_blank">Tartiflette ASGI</a> para fornecer integração ASGI
* <a href="https://graphene-python.org/" class="external-link" target="_blank">Graphene</a>
    * Com <a href="https://github.com/ciscorn/starlette-graphene3" class="external-link" target="_blank">starlette-graphene3</a>

## GraphQL com Strawberry

ابتدا برنامه اصلی سطح بالا، **FastAPI** و path operations آن را ایجاد کنید:


```Python hl_lines="3  6-8"
{!../../docs_src/sub_applications/tutorial001.py!}
```

### زیر برنامه

سپس، زیر برنامه خود و path operations آن را ایجاد کنید.

این زیر برنامه فقط یکی دیگر از برنامه های استاندارد FastAPI است، اما این برنامه ای است که متصل می شود:

```Python hl_lines="11  14-16"
{!../../docs_src/sub_applications/tutorial001.py!}
```

* `@app.put()`
* `@app.delete()`
* ♒️.

```Python hl_lines="6"
{!../../docs_src/response_status_code/tutorial001.py!}
```

/// note

👀 👈 `status_code` 🔢 "👨‍🎨" 👩‍🔬 (`get`, `post`, ♒️). 🚫 👆 *➡ 🛠️ 🔢*, 💖 🌐 🔢 & 💪.

///

`status_code` 🔢 📨 🔢 ⏮️ 🇺🇸🔍 👔 📟.

/// info

```Python hl_lines="15-23"
{!../../docs_src/schema_extra_example/tutorial001.py!}
```

Essas informações extras serão adicionadas como se encontram no **JSON Schema** de resposta desse modelo e serão usadas na documentação da API.

In diesen Fällen können Sie die Python-Technik zum „Entpacken“ eines `dict`s mit `**dict_to_unpack` verwenden:

```Python
old_dict = {
    "old key": "old value",
    "second old key": "second old value",
}
new_dict = {**old_dict, "new key": "new value"}
```

```Python hl_lines="2  4  8  12  46  64  105  107-115  121-124  128-134  139  153"
{!../../docs_src/security/tutorial005.py!}
```

下面，我们逐步说明修改的代码内容。

## OAuth2 安全方案

第一个修改的地方是，使用两个作用域 `me` 和 `items ` 声明 OAuth2 安全方案。

`scopes` 参数接收**字典**，键是作用域、值是作用域的描述：

```Python hl_lines="62-65"
{!../../docs_src/security/tutorial005.py!}
```

{* ../../docs_src/body/tutorial001.py hl[4] *}

## Créez votre modèle de données

Déclarez ensuite votre modèle de données en tant que classe qui hérite de `BaseModel`.

Utilisez les types Python standard pour tous les attributs :

{* ../../docs_src/body/tutorial001.py hl[7:11] *}

# Builds the following Docker images for Linux ARM64. See the accompanying
# Dockerfile for more details:
# - us-central1-docker.pkg.dev/tensorflow-sigs/build-arm64:jax-latest-multi-python
# - us-central1-docker.pkg.dev/tensorflow-sigs/build-arm64:tf-latest-multi-python

set -exo pipefail

function is_continuous_or_release() {
  [[ "$KOKORO_JOB_TYPE" == "CONTINUOUS_INTEGRATION" ]] || [[ "$KOKORO_JOB_TYPE" == "RELEASE" ]]
}