import jetbrains.buildServer.configs.kotlin.DslContext
import jetbrains.buildServer.configs.kotlin.buildSteps.GradleBuildStep
import model.CIBuildModel
import model.JsonBasedGradleSubprojectProvider
import model.PerformanceTestCoverage
import model.PerformanceTestType
import model.Stage
import model.StageName
import org.junit.jupiter.api.Assertions.assertEquals
import org.junit.jupiter.api.Test
import java.io.File

数据类的和运作方式与 Pydantic 模型相同。实际上，它的底层使用的也是 Pydantic。

!!! info "说明"

    注意，数据类不支持 Pydantic 模型的所有功能。

    因此，开发时仍需要使用 Pydantic 模型。

    但如果数据类很多，这一技巧能给 FastAPI 开发 Web API 增添不少助力。🤓

## `response_model` 使用数据类

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

```Python hl_lines="1  7-13  19"
{!../../../docs_src/dataclasses/tutorial002.py!}
```

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：

import jetbrains.buildServer.configs.kotlin.RelativeId
import model.CIBuildModel
import model.FlameGraphGeneration
import model.FunctionalTestBucketProvider
import model.GRADLE_BUILD_SMOKE_TEST_NAME
import model.PerformanceTestBucketProvider
import model.PerformanceTestCoverage
import model.SpecificBuild
import model.Stage
import model.StageName
import model.TestCoverage
import model.TestType

class StageProject(
    model: CIBuildModel,

## Nested Models

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel

For example, we can define an `Image` model:

=== "Python 3.10+"

org.apache.maven.internal.impl.model.DefaultModelPathTranslator
org.apache.maven.internal.impl.model.DefaultInheritanceAssembler
org.apache.maven.internal.impl.model.DefaultProfileSelector
org.apache.maven.internal.impl.model.DefaultProfileInjector
org.apache.maven.internal.impl.model.DefaultPluginManagementInjector
org.apache.maven.internal.impl.model.DefaultDependencyManagementInjector

 */

package org.jetbrains.kotlin.analysis.api.fir.generator

import org.jetbrains.kotlin.diagnostics.Severity
import org.jetbrains.kotlin.fir.checkers.generator.diagnostics.model.DiagnosticData
import org.jetbrains.kotlin.fir.checkers.generator.diagnostics.model.DiagnosticParameter
import kotlin.reflect.KType

data class HLDiagnostic(
    val original: DiagnosticData,
    val severity: Severity?,
    val className: String,

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

**FastAPI** will use that *temporal* response to extract the headers (also cookies and status code), and will put them in the final response that contains the value you returned, filtered by any `response_model`.

import gradlebuild.binarycompatibility.isSynthetic
import gradlebuild.binarycompatibility.metadata.KotlinMetadataQueries
import japicmp.model.JApiClass
import japicmp.model.JApiCompatibility
import japicmp.model.JApiConstructor
import japicmp.model.JApiField
import japicmp.model.JApiMethod
import javassist.CtBehavior
import javassist.CtClass
import javassist.CtConstructor
import javassist.CtField
import javassist.CtMember

!!! info
    ✔️ 🤯 👈 🎻 💪 🚫 🌐 Pydantic 🏷 💪.

    , 👆 5️⃣📆 💪 ⚙️ Pydantic 🏷.

    ✋️ 🚥 👆 ✔️ 📚 🎻 🤥 🤭, 👉 👌 🎱 ⚙️ 👫 🏋️ 🕸 🛠️ ⚙️ FastAPI. 👶

## 🎻 `response_model`

👆 💪 ⚙️ `dataclasses` `response_model` 🔢:

```Python hl_lines="1  7-13  19"
{!../../../docs_src/dataclasses/tutorial002.py!}
```

🎻 🔜 🔁 🗜 Pydantic 🎻.

👉 🌌, 🚮 🔗 🔜 🎦 🆙 🛠️ 🩺 👩‍💻 🔢:

但你仍然希望能够使用`response_model`过滤和转换你返回的数据。

对于这些情况，你可以使用一个`Response`参数。

## 使用 `Response` 参数

你可以在你的*路径操作函数*中声明一个`Response`类型的参数（就像你可以为cookies和头部做的那样）。

然后你可以在这个*临时*响应对象中设置`status_code`。

```Python hl_lines="1  9  12"
{!../../../docs_src/response_change_status_code/tutorial001.py!}
```

然后你可以像平常一样返回任何你需要的对象（例如一个`dict`或者一个数据库模型）。如果你声明了一个`response_model`，它仍然会被用来过滤和转换你返回的对象。