__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.

      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.9'
      - name: Setup uv
        uses: astral-sh/setup-uv@v3
        with:
          version: "0.4.15"
          enable-cache: true
          cache-dependency-glob: |

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

## GraphQL con Strawberry

然后，我又花了一些时间从用户角度（使用 FastAPI 的开发者）设计了开发者 **API**。

同时，我还在最流行的 Python 代码编辑器中测试了很多思路，包括 PyCharm、VS Code、基于 Jedi 的编辑器。

根据最新 <a href="https://www.jetbrains.com/research/python-developers-survey-2018/#development-tools" class="external-link" target="_blank">Python 开发者调研报告</a>显示，这几种编辑器覆盖了约 80% 的用户。

也就是说，**FastAPI** 针对差不多 80% 的 Python 开发者使用的编辑器进行了测试，而且其它大多数编辑器的工作方式也与之类似，因此，**FastAPI** 的优势几乎能在所有编辑器上体现。

通过这种方式，我就能找到尽可能减少代码重复的最佳方式，进而实现处处都有自动补全、类型提示与错误检查等支持。

* **➡**: `/`.
* **🛠️**: `get`.
* **🔢**: 🔢 🔛 "👨‍🎨" (🔛 `@app.get("/")`).

```Python hl_lines="7"
{!../../docs_src/first_steps/tutorial001.py!}
```

👉 🐍 🔢.

⚫️ 🔜 🤙 **FastAPI** 🕐❔ ⚫️ 📨 📨 📛 "`/`" ⚙️ `GET` 🛠️.

👉 💼, ⚫️ `async` 🔢.

---

👆 💪 🔬 ⚫️ 😐 🔢 ↩️ `async def`:

```Python hl_lines="7"
{!../../docs_src/first_steps/tutorial003.py!}
```

/// note

A **context manager** in Python is something that you can use in a `with` statement, for example, `open()` can be used as a context manager:

```Python
with open("file.txt") as file:
    file.read()
```

In recent versions of Python, there's also an **async context manager**. You would use it with `async with`:

```Python
async with lifespan(app):
    await do_stuff()
```

```Python hl_lines="6-9"
{!../../docs_src/background_tasks/tutorial001.py!}
```

## Adicionar a tarefa em segundo plano

Dentro de sua _função de operação de caminho_, passe sua função de tarefa para o objeto _tarefas em segundo plano_ com o método `.add_task()`:

```Python hl_lines="14"
{!../../docs_src/background_tasks/tutorial001.py!}
```

* 🚚 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