<img src="https://coverage-badge.samuelcolvin.workers.dev/fastapi/fastapi.svg" alt="Testabdeckung">
</a>
<a href="https://pypi.org/project/fastapi" target="_blank">
    <img src="https://img.shields.io/pypi/v/fastapi?color=%2334D058&label=pypi%20package" alt="Package-Version">
</a>
<a href="https://pypi.org/project/fastapi" target="_blank">

    assert response.json() == {"id": "foo", "value": "there goes my hero"}


def test_path_operation_img(client: TestClient):
    shutil.copy("./docs/en/docs/img/favicon.png", "./image.png")
    response = client.get("/items/foo?img=1")
    assert response.status_code == 200, response.text
    assert response.headers["Content-Type"] == "image/png"
    assert len(response.content)
    os.remove("./image.png")

    assert response.json() == {"id": "foo", "value": "there goes my hero"}


def test_path_operation_img(client: TestClient):
    shutil.copy("./docs/en/docs/img/favicon.png", "./image.png")
    response = client.get("/items/foo?img=1")
    assert response.status_code == 200, response.text
    assert response.headers["Content-Type"] == "image/png"
    assert len(response.content)
    os.remove("./image.png")

Die Datenklasse wird automatisch in eine Pydantic-Datenklasse konvertiert.

Auf diese Weise wird deren Schema in der Benutzeroberfläche der API-Dokumentation angezeigt:

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

## Datenklassen in verschachtelten Datenstrukturen { #dataclasses-in-nested-data-structures }

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

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_/tutorial002_py310.py hl[1,6:12,18] *}

Этот dataclass будет автоматически преобразован в Pydantic dataclass.

Таким образом, его схема появится в интерфейсе документации API:

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

## Dataclasses во вложенных структурах данных { #dataclasses-in-nested-data-structures }

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

A dataclass será automaticamente convertida para uma dataclass Pydantic.

Dessa forma, seu esquema aparecerá na interface de documentação da API:

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

## Dataclasses em Estruturas de Dados Aninhadas { #dataclasses-in-nested-data-structures }

El dataclass será automáticamente convertido a un dataclass de Pydantic.

De esta manera, su esquema aparecerá en la interfaz de usuario de la documentación de la API:

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

## Dataclasses en Estructuras de Datos Anidadas { #dataclasses-in-nested-data-structures }

///

## `response_model` 使用数据类

在 `response_model` 参数中使用 `dataclasses`：

{* ../../docs_src/dataclasses_/tutorial002.py hl[1,7:13,19] *}

本例把数据类自动转换为 Pydantic 数据类。

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`：

distribution = true

[tool.pdm.build]
source-includes = [
    "tests/",
    "docs_src/",
    "requirements*.txt",
    "scripts/",
    # For a test
    "docs/en/docs/img/favicon.png",
    ]

[tool.tiangolo._internal-slim-build.packages.fastapi-slim.project]
name = "fastapi-slim"

[tool.mypy]
plugins = ["pydantic.mypy"]
strict = true

[[tool.mypy.overrides]]