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`.

The dataclass will be automatically converted to a Pydantic dataclass.

This way, its schema will show up in the API docs user interface:

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

## Dataclasses in Nested Data Structures { #dataclasses-in-nested-data-structures }

You can also combine `dataclasses` with other type annotations to make nested data structures.

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

Das ist dank **Pydantic** ebenfalls möglich, da es <a href="https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel" class="external-link" target="_blank">`dataclasses` intern unterstützt</a>.

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 field  # (1)

from fastapi import FastAPI
from pydantic.dataclasses import dataclass  # (2)


@dataclass
class Item:
    name: str
    description: str | None = None


@dataclass
class Author:
    name: str
    items: list[Item] = field(default_factory=list)  # (3)


app = FastAPI()


@app.post("/authors/{author_id}/items/", response_model=Author)  # (4)

from dataclasses import field  # (1)
from typing import Union

from fastapi import FastAPI
from pydantic.dataclasses import dataclass  # (2)


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


@dataclass
class Author:
    name: str
    items: list[Item] = field(default_factory=list)  # (3)


app = FastAPI()


@app.post("/authors/{author_id}/items/", response_model=Author)  # (4)

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",

from dataclasses import dataclass

from fastapi import FastAPI


@dataclass
class Item:
    name: str
    price: float
    description: str | None = None
    tax: float | None = None


app = FastAPI()


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