## Retorno Adicional com `model`

Você pode fornecer o parâmetro `responses` aos seus *decoradores de caminho*.

Este parâmetro recebe um `dict`, as chaves são os códigos de status para cada retorno, como por exemplo `200`, e os valores são um outro `dict` com a informação de cada um deles.

Cada um desses `dict` de retorno pode ter uma chave `model`, contendo um modelo do Pydantic, assim como o `response_model`.

import okhttp3.Request
import okhttp3.containers.BasicMockServerTest.Companion.MOCKSERVER_IMAGE
import org.junit.jupiter.api.Test
import org.mockserver.client.MockServerClient
import org.mockserver.model.HttpRequest.request
import org.mockserver.model.HttpResponse.response
import org.testcontainers.containers.GenericContainer
import org.testcontainers.containers.MockServerContainer
import org.testcontainers.containers.Network

    // ----------------------------------------------------------------------
    //
    // p1 inherits from p0
    // p0 inherits from super model
    //
    // or we can show it graphically as:
    //
    // p1 ---> p0 --> super model
    //
    // ----------------------------------------------------------------------

    @Test

## Veri Filtreleme - Güvenlik

Döndürmeniz gereken veriden fazlasını döndürmediğinizden emin olmak için, [Tutorial - Response Model - Return Type](../tutorial/response-model.md){.internal-link target=_blank} sayfasını okuyun.

## Dokümantasyon Etiketleri - OpenAPI

import org.apache.maven.artifact.repository.ArtifactRepository;
import org.apache.maven.artifact.repository.ArtifactRepositoryFactory;
import org.apache.maven.model.DeploymentRepository;
import org.apache.maven.model.Repository;
import org.apache.maven.plugin.LegacySupport;
import org.apache.maven.repository.RepositorySystem;
import org.codehaus.plexus.PlexusContainer;

import org.apache.maven.api.di.Singleton;
import org.apache.maven.api.model.Plugin;
import org.apache.maven.api.model.PluginContainer;
import org.apache.maven.api.model.PluginExecution;
import org.apache.maven.api.services.LifecycleRegistry;
import org.apache.maven.api.services.PackagingRegistry;
import org.apache.maven.impl.model.DefaultLifecycleBindingsInjector;

import static org.apache.maven.api.Lifecycle.DEFAULT;

 * System.out.println(packaging.getId()); // Outputs "jar"
 * }
 * </pre>
 *
 * @see org.apache.maven.api.Session#requirePackaging(String)
 * @see org.apache.maven.api.Project#getPackaging()
 * @see org.apache.maven.api.model.Model#getPackaging()
 * @since 4.0.0
 */
@Experimental
@Immutable
public interface Packaging extends ExtensibleEnum {
    /**
     * The packaging id.
     */
    @Nonnull
    @Override
    String id();

       * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).
     * If error still persists then, apparently your CPU model does not support AVX instruction sets.
       * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).
   

    /**
     * Convert a model profile to a settings profile.
     */
    @Nonnull
    org.apache.maven.api.settings.Profile convert(@Nonnull org.apache.maven.api.model.Profile profile);

    /**
     * Convert a settings profile to a model profile.
     */
    @Nonnull
    org.apache.maven.api.model.Profile convert(@Nonnull org.apache.maven.api.settings.Profile profile);

    // ----------------------------------------------------------------------
    //
    // p1 inherits from p0
    // p0 inherits from super model
    //
    // or we can show it graphically as:
    //
    // p1 ---> p0 --> super model
    //
    // ----------------------------------------------------------------------

    @Test
    void testDependencyManagement() throws Exception {