## Exemplo

Aqui está um exemplo de *operação de rota* com parâmetros utilizando-se de alguns dos tipos acima.

```Python hl_lines="1  3  12-16"
{!../../../docs_src/extra_data_types/tutorial001.py!}
```

## Beispiel

Hier ist ein Beispiel für eine *Pfadoperation* mit Parametern, die einige der oben genannten Typen verwenden.

=== "Python 3.10+"

    ```Python hl_lines="1  3  12-16"
    {!> ../../../docs_src/extra_data_types/tutorial001_an_py310.py!}
    ```

=== "Python 3.9+"

    * 在请求和响应中被当做 `float` 一样处理。
* 您可以在这里检查所有有效的pydantic数据类型: <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">Pydantic data types</a>.

## 例子

下面是一个*路径操作*的示例，其中的参数使用了上面的一些类型。

=== "Python 3.10+"

    ```Python hl_lines="1  3  12-16"
    {!> ../../../docs_src/extra_data_types/tutorial001_an_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="1  3  12-16"

                                    "schema": {
                                        "title": "Response 500 Valid2 Valid2 Get",
                                        "type": "array",
                                        "items": {"type": "integer"},
                                    }
                                }
                            },
                        },
                    },
                }

        original_type = original_type.__pydantic_model__
    use_type = original_type
    if lenient_issubclass(original_type, BaseModel):
        original_type = cast(Type[BaseModel], original_type)
        use_type = cloned_types.get(original_type)
        if use_type is None:
            use_type = create_model(original_type.__name__, __base__=original_type)

    # TODO: in theory this would allow declaring types that could be lists of bytes
    # to be read from files and other types, but I'm not even sure it's a good idea
    # to support it as a first class "feature"
    assert is_bytes_sequence_annotation(Union[List[str], List[bytes]])


def test_is_uploadfile_sequence_annotation():
    # For coverage
    # TODO: in theory this would allow declaring types that could be lists of UploadFile

@needs_py310
def test_typing():
    types = {
        list[int]: [1, 2, 3],
        dict[str, list[int]]: {"a": [1, 2, 3], "b": [4, 5, 6]},
        set[int]: [1, 2, 3],  # `set` is converted to `list`
        tuple[int, ...]: [1, 2, 3],  # `tuple` is converted to `list`
    }
    for test_type, expect in types.items():
        app = FastAPI()

        @app.post("/", response_model=test_type)
        def post_endpoint(input: test_type):

Hug was one of the first frameworks to implement the declaration of API parameter types using Python type hints. This was a great idea that inspired other tools to do the same.

It used custom types in its declarations instead of standard Python types, but it was still a huge step forward.

It also was one of the first frameworks to generate a custom schema declaring the whole API in JSON.

                        "type": "string"
                    },
                    "type": {
                        "title": "Error Type",
                        "type": "string"
                    }
                }
            },
            "HTTPValidationError": {
                "title": "HTTPValidationError",
                "type": "object",
                "properties": {

        annotation: Optional[Any] = None,
        embed: bool = False,
        media_type: str = "application/json",
        alias: Optional[str] = None,
        alias_priority: Union[int, None] = _Unset,
        # TODO: update when deprecating Pydantic v1, import these types
        # validation_alias: str | AliasPath | AliasChoices | None
        validation_alias: Union[str, None] = None,