import jetbrains.buildServer.configs.kotlin.BuildStep
import jetbrains.buildServer.configs.kotlin.buildSteps.script
import model.CIBuildModel
import model.Stage
import model.StageName

class FlakyTestQuarantine(model: CIBuildModel, stage: Stage, os: Os, arch: Arch = Arch.AMD64) : BaseGradleBuildType(stage = stage, init = {
    id("${model.projectId}_FlakyQuarantine_${os.asName()}_${arch.asName()}")

class SingleTestProjectBucket(val testProject: String, val scenarios: List<Scenario>) : PerformanceTestBucket {
    override
    fun createPerformanceTestsFor(model: CIBuildModel, stage: Stage, performanceTestCoverage: PerformanceTestCoverage, bucketIndex: Int): PerformanceTest = createPerformanceTest(
        model,
        performanceTestCoverage,
        stage,
        bucketIndex,
        "Performance tests for $testProject",

import jetbrains.buildServer.configs.kotlin.ReuseBuilds
import model.CIBuildModel
import model.PerformanceTestType
import projects.PerformanceTestProject

class PerformanceTestsPass(model: CIBuildModel, performanceTestProject: PerformanceTestProject) : BaseGradleBuildType(
    failStage = performanceTestProject.spec.failsStage,
    init = {
        id("${performanceTestProject.spec.asConfigurationId(model)}_Trigger")

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.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

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

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

    , 👆 5️⃣📆 💪 ⚙️ Pydantic 🏷.

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

## 🎻 `response_model`

👆 💪 ⚙️ `dataclasses` `response_model` 🔢:

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

🎻 🔜 🔁 🗜 Pydantic 🎻.

👉 🌌, 🚮 🔗 🔜 🎦 🆙 🛠️ 🩺 👩‍💻 🔢:

Like `item.model_dump(exclude_unset=True)`.

!!! info
    In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

    The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

    CompileAll {
        override fun create(model: CIBuildModel, stage: Stage): BaseGradleBuildType {
            return CompileAll(model, stage)
        }
    },
    SanityCheck {
        override fun create(model: CIBuildModel, stage: Stage): BaseGradleBuildType {
            return SanityCheck(model, stage)
        }
    },
    BuildDistributions {
        override fun create(model: CIBuildModel, stage: Stage): BaseGradleBuildType {

            model.buildTypes.map { it.name }
        )
    }

    @Test
    fun `promotion project has expected build types for other branches`() {
        val model = setupModelFor("release")

        assertEquals("Promotion", model.name)
        assertEquals(11, model.buildTypes.size)
        assertEquals(