kotlin-junit5 = { module = "org.jetbrains.kotlin:kotlin-test-junit5", version.ref = "kotlin" }
kotlin-reflect = { module = "org.jetbrains.kotlin:kotlin-reflect", version.ref = "kotlin" }
kotlin-stdlib = { module = "org.jetbrains.kotlin:kotlin-stdlib", version.ref = "kotlin" }
kotlin-stdlib-osgi = { module = "org.jetbrains.kotlin:kotlin-osgi-bundle", version.ref = "kotlin" }

        }

    private
    fun fetchLatestKotlinVersions() =
        fetchAndSelectKotlinVersions(
            minimumSupported.get(),
            "https://repo1.maven.org/maven2/org/jetbrains/kotlin/kotlin-stdlib/maven-metadata.xml"
        )

    private
    fun fetchAndSelectKotlinVersions(minimumSupported: String, mavenMetadataUrl: String): List<String> {

private val Project.kotlinOsgi: MinimalExternalModuleDependency
  get() =
    extensions
      .getByType(VersionCatalogsExtension::class.java)
      .named("libs")
      .findLibrary("kotlin.stdlib.osgi")
      .get()

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

Das ist dank **Pydantic** ebenfalls möglich, da es [„`dataclasses` intern unterstützt“](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel).

Auch wenn im obigen Code Pydantic nicht explizit vorkommt, verwendet FastAPI Pydantic, um diese Standard-Datenklassen in Pydantics eigene Variante von Datenklassen zu konvertieren.

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

This is still supported thanks to **Pydantic**, as it has [internal support for `dataclasses`](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel).

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

limitations under the License.
==============================================================================*/

#include <limits.h>
#include <memory.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <unistd.h>

#ifdef _WIN32
#include <process.h>
#endif

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_experimental.h"
#include "tensorflow/c/env.h"

            <option value="$PROJECT_DIR$/platforms/core-configuration/project-features-demos" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/stdlib-kotlin-extensions" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/stdlib-serialization-codecs" />
            <option value="$PROJECT_DIR$/platforms/core-execution/build-cache" />

Это по-прежнему поддерживается благодаря **Pydantic**, так как в нём есть [встроенная поддержка `dataclasses`](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel).

Так что даже если в коде выше Pydantic не используется явно, FastAPI использует Pydantic, чтобы конвертировать стандартные dataclasses в собственный вариант dataclasses от Pydantic.

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

Bu destek hâlâ **Pydantic** sayesinde vardır; çünkü Pydantic, [`dataclasses` için dahili destek](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel) sunar.

Yani yukarıdaki kod Pydantic'i doğrudan kullanmasa bile, FastAPI bu standart dataclass'ları Pydantic'in kendi dataclass biçimine dönüştürmek için Pydantic'i kullanmaktadır.

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

Це підтримується завдяки **Pydantic**, адже він має [внутрішню підтримку `dataclasses`](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel).

Тож навіть із наведеним вище кодом, який явно не використовує Pydantic, FastAPI використовує Pydantic, щоб перетворити стандартні dataclasses у власний варіант dataclasses Pydantic.