# Module okhttp

# Module okhttp-coroutines

import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.model.Model;

/**
 * Provides the super POM that all models implicitly inherit from.
 *
 * @since 4.0.0
 */
@Experimental
public interface SuperPomProvider extends Service {

    /**
     * Gets the super POM for the specified model version.
     *
     * @param version The model version to retrieve the super POM for (e.g. "4.0.0"), must not be {@code null}.

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Crea tu modelo de datos { #create-your-data-model }

Luego, declaras tu modelo de datos como una clase que hereda de `BaseModel`.

Usa tipos estándar de Python para todos los atributos:

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}

 *
 * <p>This factory is responsible for instantiating concrete toolchain implementations
 * based on toolchain model configurations or default settings.</p>
 *
 * @since 4.0.0
 */
@Experimental
@Consumer
public interface ToolchainFactory {
    /**
     * Creates a toolchain instance from the provided model configuration.
     *

 */
package org.apache.maven.model.composition;

import java.util.List;

import org.apache.maven.model.DependencyManagement;
import org.apache.maven.model.Model;
import org.apache.maven.model.building.ModelBuildingRequest;
import org.apache.maven.model.building.ModelProblemCollector;

/**
 * Handles the import of dependency management from other models into the target model.
 *

      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>2.0.0</version>
          <basedir>${project.basedir}/../../api/maven-api-settings</basedir>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <models>
            <model>src/main/mdo/settings.mdo</model>
          </models>
          <params>

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.model.building;

/**
 * Caches auxiliary data used during model building like already processed raw/effective models. The data in the cache
 * is meant for exclusive consumption by the model builder and is opaque to the cache implementation. The cache key is

client = TestClient(app)


def test_get_enums_alexnet():
    response = client.get("/models/alexnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "alexnet", "message": "Deep Learning FTW!"}


def test_get_enums_lenet():
    response = client.get("/models/lenet")
    assert response.status_code == 200

* Recuperar los datos almacenados.
* Poner esos datos en un modelo de Pydantic.
* Generar un `dict` sin valores por defecto del modelo de entrada (usando `exclude_unset`).
    * De esta manera puedes actualizar solo los valores realmente establecidos por el usuario, en lugar de sobrescribir valores ya almacenados con valores por defecto en tu modelo.