### 总结

总的来说，你就像声明函数的参数类型一样只声明了**一次**请求参数、请求体等的类型。

你使用了标准的现代 Python 类型来完成声明。

你不需要去学习新的语法、了解特定库的方法或类，等等。

只需要使用标准的 **Python 及更高版本**。

举个例子，比如声明 `int` 类型：

```Python
item_id: int
```

或者一个更复杂的 `Item` 模型：

```Python
item: Item
```

......在进行一次声明之后，你将获得：

* 编辑器支持，包括：
    * 自动补全
    * 类型检查
* 数据校验：

### Faite en python moderne

Tout est basé sur la déclaration de type standard de **Python 3.8** (grâce à Pydantic). Pas de nouvelles syntaxes à apprendre. Juste du Python standard et moderne.

Si vous souhaitez un rappel de 2 minutes sur l'utilisation des types en Python (même si vous ne comptez pas utiliser FastAPI), jetez un oeil au tutoriel suivant: [Python Types](python-types.md){.internal-link target=_blank}.

そこで、以下のようなPydanticオブジェクト`user_in`を作成すると:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

そして呼び出すと:

```Python
user_dict = user_in.dict()
```

これで変数`user_dict`のデータを持つ`dict`ができました。（これはPydanticモデルのオブジェクトの代わりに`dict`です）。

そして呼び出すと:

```Python
print(user_dict)
```

以下のようなPythonの`dict`を得ることができます:

```Python
{
    'username': 'john',

### Apenas Python moderno { #just-modern-python }

Tudo é baseado no padrão das declarações de **tipos do Python** (graças ao Pydantic). Nenhuma sintaxe nova para aprender. Apenas o padrão moderno do Python.

Se você precisa refrescar a memória rapidamente sobre como usar tipos do Python (mesmo que você não use o FastAPI), confira esse rápido tutorial: [Tipos do Python](python-types.md){.internal-link target=_blank}.

<div class="termy">

```console
$ which python

/home/user/code/awesome-project/.venv/bin/python
```

</div>

If it shows the `python` binary at `.venv/bin/python`, inside of your project (in this case `awesome-project`), then it worked. 🎉

////

//// tab | Windows PowerShell

<div class="termy">

```console
$ Get-Command python

So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

and then we call:

```Python
user_dict = user_in.model_dump()
```

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

And if we call:

```Python
print(user_dict)
```

## Managing hermetic Python

To make sure that TensorFlow's build is reproducible, behaves uniformly across
supported platforms (Linux, Windows, MacOS) and is properly isolated from
specifics of a local system, we rely on hermetic Python (see
[rules_python](https://github.com/bazelbuild/rules_python)) for all build
and test commands executed via Bazel. This means that your system Python
installation will be ignored during the build and Python interpreter itself

/// info

To receive uploaded files, first install <a href="https://github.com/Kludex/python-multipart" class="external-link" target="_blank">`python-multipart`</a>.

Make sure you create a [virtual environment](../virtual-environments.md){.internal-link target=_blank}, activate it, and then install it, for example:

```console
$ pip install python-multipart
```

This is because uploaded files are sent as "form data".

///

Para receber arquivos enviados, primeiro instale o <a href="https://github.com/Kludex/python-multipart" class="external-link" target="_blank">`python-multipart`</a>.

Garanta que você criou um [ambiente virtual](../virtual-environments.md){.internal-link target=_blank}, o ativou e então o instalou, por exemplo:

```console
$ pip install python-multipart
```

# pylint: disable=g-bad-import-order,protected-access,g-import-not-at-top
from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
from tensorflow.python.tools import module_util as _module_util
from tensorflow.python.platform import tf_logging as _logging
from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# API IMPORTS PLACEHOLDER