* 🚚 2️⃣ 🎓 🧾 🕸 🔢 🔗.

---

👥 🖌 🧽, ✋️ 👆 ⏪ 🤚 💭 ❔ ⚫️ 🌐 👷.

🔄 🔀 ⏸ ⏮️:

```Python
    return {"item_name": item.name, "item_id": item_id}
```

...⚪️➡️:

```Python
        ... "item_name": item.name ...
```

...:

```Python
        ... "item_price": item.price ...
```

...& 👀 ❔ 👆 👨‍🎨 🔜 🚘-🏁 🔢 & 💭 👫 🆎:

### Environnement virtuel avec `venv`

Vous pouvez créer un environnement virtuel dans un répertoire en utilisant le module `venv` de Python :

<div class="termy">

```console
$ python -m venv env
```

</div>

Cela va créer un répertoire `./env/` avec les binaires Python et vous pourrez alors installer des paquets pour cet environnement isolé.

### Activer l'environnement

Activez le nouvel environnement avec :

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cpu
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow_cpu"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=tensorflow/build:2.18-python${TFCI_PYTHON_VERSION}
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--build-arg PYTHON_VERSION=python$TFCI_PYTHON_VERSION --target=devel tensorflow/tools/tf_sig_build_dockerfiles"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-x86_64"
TFCI_OUTPUT_DIR=build_output

        }

        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"}
```