2. `pydantic.dataclasses` — полностью совместимая замена (drop-in replacement) для `dataclasses`.

3. Dataclass `Author` содержит список dataclass `Item`.

4. Dataclass `Author` используется в параметре `response_model`.

5. Вы можете использовать и другие стандартные аннотации типов вместе с dataclasses в качестве тела запроса.

    В этом случае это список dataclass `Item`.

2. `pydantic.dataclasses` es un reemplazo directo para `dataclasses`.

3. El dataclass `Author` incluye una lista de dataclasses `Item`.

4. El dataclass `Author` se usa como el parámetro `response_model`.

5. Puedes usar otras anotaciones de tipos estándar con dataclasses como el request body.

    En este caso, es una lista de dataclasses `Item`.

3. A dataclass `Author` inclui uma lista de dataclasses `Item`.

4. A dataclass `Author` é usada como o parâmetro `response_model`.

5. Você pode usar outras anotações de tipo padrão com dataclasses como o corpo da requisição.

    Neste caso, é uma lista de dataclasses `Item`.

6. Aqui estamos retornando um dicionário que contém `items`, que é uma lista de dataclasses.

<img src="/img/tutorial/dataclasses/image01.png">

## 在嵌套数据结构中使用数据类

您还可以把 `dataclasses` 与其它类型注解组合在一起，创建嵌套数据结构。

还有一些情况也可以使用 Pydantic 的 `dataclasses`。例如，在 API 文档中显示错误。

本例把标准的 `dataclasses` 直接替换为 `pydantic.dataclasses`：

```{ .python .annotate hl_lines="1  5  8-11  14-17  23-25  28" }
{!../../docs_src/dataclasses_/tutorial003.py!}
```

1. 本例依然要从标准的 `dataclasses` 中导入 `field`；

2. 使用 `pydantic.dataclasses` 直接替换 `dataclasses`；

from dataclasses import dataclass, field
from typing import Union

from fastapi import FastAPI


@dataclass
class Item:
    name: str
    price: float
    tags: list[str] = field(default_factory=list)
    description: Union[str, None] = None
    tax: Union[float, None] = None


app = FastAPI()


@app.get("/items/next", response_model=Item)
async def read_next_item():
    return {
        "name": "Island In The Moon",

            exclude_none=exclude_none,
            exclude_defaults=exclude_defaults,
            sqlalchemy_safe=sqlalchemy_safe,
        )
    if dataclasses.is_dataclass(obj):
        assert not isinstance(obj, type)
        obj_dict = dataclasses.asdict(obj)
        return jsonable_encoder(
            obj_dict,
            include=include,
            exclude=exclude,
            by_alias=by_alias,

from dataclasses import dataclass
from typing import Union

from fastapi import FastAPI


@dataclass
class Item:
    name: str
    price: float
    description: Union[str, None] = None
    tax: Union[float, None] = None


app = FastAPI()


@app.post("/items/")
async def create_item(item: Item):

from dataclasses import dataclass, field

from fastapi import FastAPI


@dataclass
class Item:
    name: str
    price: float
    tags: list[str] = field(default_factory=list)
    description: str | None = None
    tax: float | None = None


app = FastAPI()


@app.get("/items/next", response_model=Item)
async def read_next_item():
    return {
        "name": "Island In The Moon",
        "price": 12.99,

from typing import Optional

import pytest
from fastapi import FastAPI
from fastapi.exceptions import ResponseValidationError
from fastapi.testclient import TestClient
from pydantic.dataclasses import dataclass

app = FastAPI()


@dataclass
class Item:
    name: str
    price: Optional[float] = None
    owner_ids: Optional[list[int]] = None


@app.get("/items/invalid", response_model=Item)
def get_invalid():

from dataclasses import dataclass
from datetime import datetime
from typing import Optional

from fastapi import FastAPI
from fastapi.testclient import TestClient

app = FastAPI()


@dataclass
class Item:
    name: str
    date: datetime
    price: Optional[float] = None
    owner_ids: Optional[list[int]] = None


@app.get("/items/valid", response_model=Item)
def get_valid():