needs_py310 = pytest.mark.skipif(
    sys.version_info < (3, 10), reason="requires python3.10+"
)
needs_pydanticv2 = pytest.mark.skipif(not PYDANTIC_V2, reason="requires Pydantic v2")

                "BaseUser": {
                    "title": "BaseUser",
                    "required": IsOneOf(
                        ["username", "email", "full_name"],
                        # TODO: remove when deprecating Pydantic v1
                        ["username", "email"],
                    ),
                    "type": "object",
                    "properties": {
                        "username": {"title": "Username", "type": "string"},

    * O `Decimal` padrão do Python.
    * Em requisições e respostas será representado como um `float`.
* Você pode checar todos os tipos de dados válidos do Pydantic aqui: <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">Tipos de dados do Pydantic</a>.

## 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"

If you have a group of related **header parameters**, you can create a **Pydantic model** to declare them.

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Header Parameters with a Pydantic Model

                    "input": "foo",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["path", "item_id"],
                    "msg": "value is not a valid integer",
                    "type": "type_error.integer",

Genauso würde die Datenbank kein Pydantic-Modell (ein Objekt mit Attributen) akzeptieren, sondern nur ein `dict`.

Sie können für diese Fälle `jsonable_encoder` verwenden.

Es nimmt ein Objekt entgegen, wie etwa ein Pydantic-Modell, und gibt eine JSON-kompatible Version zurück:

//// tab | Python 3.10+

```Python hl_lines="4  21"

一般的なユースケースはこのドキュメントで示されているように`example`を追加することです。

JSON Schemaの追加情報を宣言する方法はいくつかあります。

## Pydanticの`schema_extra`

<a href="https://docs.pydantic.dev/latest/concepts/json_schema/#schema-customization" class="external-link" target="_blank">Pydanticのドキュメント: スキーマのカスタマイズ</a>で説明されているように、`Config`と`schema_extra`を使ってPydanticモデルの例を宣言することができます:

```Python hl_lines="15 16 17 18 19 20 21 22 23"

from typing import List, Tuple

from dirty_equals import IsDict
from fastapi import FastAPI, Form
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class ItemGroup(BaseModel):
    items: List[Tuple[str, str]]


class Coordinate(BaseModel):
    x: float
    y: float


@app.post("/model-with-tuple/")
def post_model_with_tuple(item_group: ItemGroup):
    return item_group

                                        ],
                                    }
                                )
                                | IsDict(
                                    # TODO: remove when deprecating Pydantic v1
                                    {
                                        "allOf": [
                                            {"$ref": "#/components/schemas/Item"}
                                        ],

                                        ],
                                    }
                                )
                                | IsDict(
                                    # TODO: remove when deprecating Pydantic v1
                                    {
                                        "allOf": [
                                            {"$ref": "#/components/schemas/Item"}
                                        ],