## 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 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;

    /**
     * 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 {

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

    @Test

 *  @author Victor Jonsson, http://victorjonsson.se
 *  @license MIT
 */

        PrunedTag[] tags = PrunedTag.parse("div[data-toggle=modal].modal#main-modal");
        assertEquals(1, tags.length);
        PrunedTag tag = tags[0];
        assertEquals("div", tag.toString().split(", ")[0].split("=")[1]);
        assertTrue(tag.toString().contains("id=main-modal"));
        assertTrue(tag.toString().contains("css=modal"));
        assertTrue(tag.toString().contains("attrName=data-toggle"));

			</plugin>
			<plugin>
				<artifactId>maven-jar-plugin</artifactId>
				<version>3.4.2</version>
				<configuration>
					<archive>
						<manifestEntries>
							<Automatic-Module-Name>org.codelibs.corelib</Automatic-Module-Name>
						</manifestEntries>
					</archive>
				</configuration>
			</plugin>
			<plugin>
				<artifactId>maven-surefire-plugin</artifactId>
				<version>3.5.3</version>
			</plugin>

       * 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).
   

 *  @author Victor Jonsson, http://victorjonsson.se
 *  @license MIT
 */