}
                """
            )
            withDirectory("v1/src/main").v1()
            withDirectory("v2/src/main").v2()
            val sourceRoots = if (File(withDirectory("v2/src/main"), "java").exists()) {
                "v2/src/main/java"
            } else {
                "v2/src/main/kotlin"
            }
            withDirectory("binary-compatibility").apply {

    <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">

            <url>https://download.jetbrains.com/teamcity-repository</url>
        </pluginRepository>
    </pluginRepositories>

    <build>
        <sourceDirectory>${project.basedir}/src/main/kotlin</sourceDirectory>
        <testSourceDirectory>${project.basedir}/src/test/kotlin</testSourceDirectory>
        <plugins>
            <plugin>
                <artifactId>kotlin-maven-plugin</artifactId>

{* ../../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`：

    }
}

// We support multiple accepted public api changes JSON files, but "accepted-public-api-changes.json"
// is the "main" one that is also shown in HTML report
def apiChangesJsonDirectory = layout.projectDirectory.dir("src/changes/accepted-changes")

def compatibilityBaselineVersion = gradleModule.identity.releasedVersions.get().mostRecentRelease.version

def ARTIFACT_TYPE = Attribute.of('artifactType', String)

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.