Importieren Sie `BaseSettings` aus Pydantic und erstellen Sie eine Unterklasse, ganz ähnlich wie bei einem Pydantic-Modell.

Auf die gleiche Weise wie bei Pydantic-Modellen deklarieren Sie Klassenattribute mit Typannotationen und möglicherweise Defaultwerten.

Sie können dieselben Validierungs-Funktionen und -Tools verwenden, die Sie für Pydantic-Modelle verwenden, z. B. verschiedene Datentypen und zusätzliche Validierungen mit `Field()`.

 * under the License.
 */
package org.apache.maven.repository.internal.relocation;

import javax.inject.Named;
import javax.inject.Singleton;

import org.apache.maven.model.DistributionManagement;
import org.apache.maven.model.Model;
import org.apache.maven.model.Relocation;
import org.apache.maven.repository.internal.MavenArtifactRelocationSource;
import org.apache.maven.repository.internal.RelocatedArtifact;

	"gorm.io/gorm/utils"
)

func SetupUpdateReflectValue(db *gorm.DB) {
	if db.Error == nil && db.Statement.Schema != nil {
		if !db.Statement.ReflectValue.CanAddr() || db.Statement.Model != db.Statement.Dest {
			db.Statement.ReflectValue = reflect.ValueOf(db.Statement.Model)
			for db.Statement.ReflectValue.Kind() == reflect.Ptr {
				db.Statement.ReflectValue = db.Statement.ReflectValue.Elem()
			}

import gradlebuild.docs.XmlSpecification
import gradlebuild.docs.dsl.source.TypeNameResolver
import gradlebuild.docs.dsl.source.model.ClassMetaData
import gradlebuild.docs.dsl.source.model.EnumConstantMetaData
import gradlebuild.docs.dsl.source.model.MethodMetaData
import gradlebuild.docs.model.ClassMetaDataRepository

class JavadocLinkConverterTest extends XmlSpecification {
    final LinkRenderer linkRenderer = Mock()

  int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

// Runs model as is if output is a scalar,
// else sums the output tensor before returning.
absl::Status RunAndMaybeSum(AbstractContext* ctx, Model forward,
                            absl::Span<AbstractTensorHandle* const> inputs,
                            absl::Span<AbstractTensorHandle*> outputs,

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

This ADR does not specify the owner of this new architecture module. However, as a separate module, it can be assigned ownership independently of the other core services.

## Status

ACCEPTED

## Consequences

- Introduce the module and move the services and their implementations.

     * @param result The artifact descriptor result, never {@code null}.
     * @param model The artifact model, never {@code null}.
     * @return The {@link Artifact} to relocate to, or {@code null} if no relocation wanted.
     */
    Artifact relocatedTarget(RepositorySystemSession session, ArtifactDescriptorResult result, Model model)
            throws ArtifactDescriptorException;

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",

 * under the License.
 */
package org.apache.maven.model.profile;

import java.util.Collections;
import java.util.List;

import org.apache.maven.model.Activation;
import org.apache.maven.model.Profile;
import org.apache.maven.model.building.ModelProblemCollector;
import org.apache.maven.model.building.SimpleProblemCollector;
import org.apache.maven.model.profile.activation.ProfileActivator;
import org.junit.jupiter.api.Test;