```JSON
{"item_id":3}
```

/// check | Revisa

Nota que el valor que tu función recibió (y devolvió) es `3`, como un `int` de Python, no un string `"3"`.

Entonces, con esa declaración de tipo, **FastAPI** te ofrece <abbr title="converting the string that comes from an HTTP request into Python data">"parsing"</abbr> automático de requests.

///

## Validación de datos

//// tab | Python 3.10+

В Python 3.9 или выше, `Annotated` является частью стандартной библиотеки, таким образом вы можете импортировать его из `typing`.

```Python hl_lines="1  3"
{!> ../../docs_src/query_params_str_validations/tutorial002_an_py310.py!}
```

////

//// tab | Python 3.8+

        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - uses: actions/checkout@v5
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python-version }}
      - name: Setup uv
        uses: astral-sh/setup-uv@v6
        with:
          version: "0.4.15"
          enable-cache: true

      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - uses: actions/checkout@v5
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - name: Setup uv
        uses: astral-sh/setup-uv@v6
        with:
          version: "0.4.15"
          enable-cache: true
          cache-dependency-glob: |

      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - uses: actions/checkout@v5
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - name: Setup uv
        uses: astral-sh/setup-uv@v6
        with:
          version: "0.4.15"
          enable-cache: true
          cache-dependency-glob: |

따라서, 다음과 같이 Pydantic 객체 `user_in`을 생성할 수 있습니다:

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

그 다음, 다음과 같이 호출합니다:

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

이제 변수 `user_dict`에 데이터가 포함된 `dict`를 가지게 됩니다(이는 Pydantic 모델 객체가 아닌 `dict`입니다).

그리고 다음과 같이 호출하면:

```Python
print(user_dict)
```

Python의 `dict`가 다음과 같이 출력됩니다:

```Python

Así que, si creamos un objeto Pydantic `user_in` como:

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

y luego llamamos a:

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

ahora tenemos un `dict` con los datos en la variable `user_dict` (es un `dict` en lugar de un objeto modelo Pydantic).

Y si llamamos a:

```Python
print(user_dict)
```

</div>

////

### Lendo variáveis de ambiente com Python

Você também pode criar variáveis de ambiente fora do Python, no terminal (ou com qualquer outro método), e realizar a leitura delas no Python.

Por exemplo, você pode definir um arquivo `main.py` com o seguinte código:

```Python hl_lines="3"
import os

name = os.getenv("MY_NAME", "World")
print(f"Hello {name} from Python")
```

/// tip | Dica

## 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

<div class="termy">

```console
$ which python

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

</div>

Nếu nó hiển thị `python` binary tại `.venv/bin/python`, trong dự án của bạn (trong trường hợp `awesome-project`), thì tức là nó hoạt động. 🎉

////

//// tab | Windows PowerShell

<div class="termy">

```console
$ Get-Command python