* given user when the target object's ACL has local groups. Local groups
     * are not listed in a user's group membership (e.g. as represented by the
     * tokenGroups constructed attribute retrived via LDAP).
     *
     * Domain groups nested inside a local group are currently not expanded. In
     * this case the key (SID) type will be SID_TYPE_DOM_GRP rather than
     * SID_TYPE_USER.

# here allows us to override the behavior if needed.
config_setting(
    name = "no_xla_deps_in_cuda",
    define_values = {"no_xla_deps_in_cuda": "true"},
    visibility = ["//visibility:public"],
)

# Crosses between framework_shared_object and a bunch of other configurations
# due to limitations in nested select() statements.
config_setting(
    name = "framework_shared_object",

import com.google.caliper.Param;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import org.jspecify.annotations.NullUnmarked;

@SuppressWarnings("unused") // Nested enums used reflectively in setUp.
@NullUnmarked
public class EnumsBenchmark {

  @Param({"Small", "Medium", "Large"})
  String enumSize;

  @Param({"0.2", "0.8"})
  float hitRate;

	Owner Owner

	// Container for one or more buckets.
	Buckets struct {
		Buckets []Bucket `xml:"Bucket"`
	} // Buckets are nested
}

// Upload container for in progress multipart upload
type Upload struct {
	Key          string
	UploadID     string `xml:"UploadId"`
	Initiator    Initiator
	Owner        Owner
	StorageClass string

import com.google.caliper.Param;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import org.jspecify.annotations.NullUnmarked;

@SuppressWarnings("unused") // Nested enums used reflectively in setUp.
@NullUnmarked
public class EnumsBenchmark {

  @Param({"Small", "Medium", "Large"})
  String enumSize;

  @Param({"0.2", "0.8"})
  float hitRate;

{* ../../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.

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

Você também pode combinar `dataclasses` com outras anotações de tipo para criar estruturas de dados aninhadas.

Em alguns casos, você ainda pode ter que usar a versão do Pydantic das `dataclasses`. Por exemplo, se você tiver erros com a documentação da API gerada automaticamente.

Таким чином його схема з'явиться в інтерфейсі користувача документації API:

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

## Dataclasses у вкладених структурах даних { #dataclasses-in-nested-data-structures }

Можна поєднувати `dataclasses` з іншими анотаціями типів, щоб створювати вкладені структури даних.

        assertEquals("", item.getPath());
    }

    public void test_deeplyNestedPath() {
        final String deepPath = "/level1/level2/level3/level4/level5/";
        final StorageItem item = new StorageItem("nested.txt", deepPath, false, 256L, null, "bmVzdGVkLnR4dA==");

        assertEquals(deepPath, item.getPath());
    }

    public void test_specialCharactersInName() {

  *  HorizontalPodAutoscaler was Beta in 1.1 and is GA in 1.2 .
     * <code>apiVersion: autoscaling/v1 </code>is now available.  Changes in this version are:
        * Field CPUUtilization which was a nested structure CPUTargetUtilization in
HorizontalPodAutoscalerSpec was replaced by TargetCPUUtilizationPercentage
which is an integer.
        * ScaleRef of type SubresourceReference in HorizontalPodAutoscalerSpec which