*
 * <h3>Upgrade Strategies</h3>
 * <p>The tool uses a strategy pattern to handle different types of upgrades:</p>
 * <ul>
 *   <li>{@link org.apache.maven.cling.invoker.mvnup.goals.ModelUpgradeStrategy} - Upgrades POM model versions (4.0.0 → 4.1.0)</li>
 *   <li>{@link org.apache.maven.cling.invoker.mvnup.goals.PluginUpgradeStrategy} - Upgrades plugin versions for Maven 4 compatibility</li>

        value: str | Missing = Missing()

    class Model(BaseModel):
        model_config = ConfigDict(
            arbitrary_types_allowed=True,
        )
        value: str | Missing = Missing()
        embedded_model: EmbeddedModel = EmbeddedModel()

    @app.post("/")
    def foo(req: Model) -> dict[str, str | None]:
        return {
            "value": req.value or None,

 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.repository.internal;

import org.apache.maven.model.Repository;
import org.eclipse.aether.artifact.Artifact;
import org.eclipse.aether.artifact.DefaultArtifact;
import org.eclipse.aether.repository.RemoteRepository;
import org.eclipse.aether.repository.RepositoryPolicy;

Muchos paquetes que lo simplifican tienen que hacer muchos compromisos con el modelo de datos, la base de datos y las funcionalidades disponibles. Y algunos de estos paquetes que simplifican las cosas demasiado en realidad tienen fallos de seguridad en el fondo.

---

**FastAPI** no hace ningún compromiso con ninguna base de datos, modelo de datos o herramienta.

Uygulamanızın alabileceği veriler için örnekler (examples) tanımlayabilirsiniz.

Bunu yapmanın birkaç yolu var.

## Pydantic modellerinde ek JSON Schema verisi { #extra-json-schema-data-in-pydantic-models }

Oluşturulan JSON Schema’ya eklenecek şekilde bir Pydantic model için `examples` tanımlayabilirsiniz.

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

       * 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).
   

import org.apache.maven.impl.InternalSession;
import org.apache.maven.impl.cache.DefaultRequestCacheFactory;
import org.apache.maven.internal.impl.DefaultSession;
import org.apache.maven.model.Dependency;
import org.apache.maven.model.Repository;
import org.apache.maven.model.RepositoryPolicy;
import org.apache.maven.plugin.LegacySupport;
import org.apache.maven.project.artifact.DefaultMetadataSource;

        if (defaultWhenNoOptionsSpecified
                && options.infer().isEmpty()
                && options.model().isEmpty()
                && options.plugins().isEmpty()
                && options.model().isEmpty()) {
            return true;
        }

        return false;
    }

    /**
     * Gets a description of what this strategy does.
     *

{* ../../docs_src/response_change_status_code/tutorial001_py310.py hl[1,9,12] *}

Y luego puedes devolver cualquier objeto que necesites, como lo harías normalmente (un `dict`, un modelo de base de datos, etc).

Y si declaraste un `response_model`, todavía se utilizará para filtrar y convertir el objeto que devolviste.

 * in compliance with, at your election, the Elastic License 2.0 or the Server
 * Side Public License, v 1.
 */

package org.elasticsearch.gradle.internal;

/**
 * This class models the different Docker base images that are used to build Docker distributions of Elasticsearch.
 */
public enum DockerBase {
    CENTOS("centos:8", ""),

    // "latest" here is intentional, since the image name specifies "8"