In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

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

1. We still import `field` from standard `dataclasses`.

API 文档中也会显示相关概图：

<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`；

```Python hl_lines="1  7-12  19-20"
{!../../../docs_src/dataclasses/tutorial001.py!}
```

👉 🐕‍🦺 👏 **Pydantic**, ⚫️ ✔️ <a href="https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel" class="external-link" target="_blank">🔗 🐕‍🦺 `dataclasses`</a>.

```Python hl_lines="1  7-12  19-20"
{!../../../docs_src/dataclasses/tutorial001.py!}
```

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

from dataclasses import field  # (1)
from typing import List, 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()

    runTest("analysis/analysis-api/testData/components/typeProvider/haveCommonSubtype/collections.kt");
  }

  @Test
  @TestMetadata("dataClasses.kt")
  public void testDataClasses() {
    runTest("analysis/analysis-api/testData/components/typeProvider/haveCommonSubtype/dataClasses.kt");
  }

  @Test
  @TestMetadata("enums.kt")
  public void testEnums() {

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 typing import List, 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, field
from typing import List, 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 {

            # TODO: remove when deprecating Pydantic v1
            custom_encoder=encoders,
            sqlalchemy_safe=sqlalchemy_safe,
        )
    if dataclasses.is_dataclass(obj):
        obj_dict = dataclasses.asdict(obj)
        return jsonable_encoder(
            obj_dict,
            include=include,
            exclude=exclude,
            by_alias=by_alias,