import org.apache.maven.execution.MavenSession;
import org.apache.maven.impl.DefaultRepositoryFactory;
import org.apache.maven.impl.InternalSession;
import org.apache.maven.impl.model.DefaultInterpolator;
import org.apache.maven.impl.resolver.scopes.Maven4ScopeManagerConfiguration;
import org.apache.maven.internal.impl.DefaultSession;
import org.codehaus.plexus.PlexusContainer;

<img src="/img/python-types/image06.png">

Notice that this means "`one_person` is an **instance** of the class `Person`".

It doesn't mean "`one_person` is the **class** called `Person`".

## Pydantic models { #pydantic-models }

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

authors:
  - family-names: Abadi
    given-names: Martín
  - family-names: Agarwal
    given-names: Ashish

import org.apache.maven.api.MonotonicClock;
import org.apache.maven.artifact.repository.ArtifactRepository;
import org.apache.maven.eventspy.internal.EventSpyDispatcher;
import org.apache.maven.model.Profile;
import org.apache.maven.model.root.RootLocator;
import org.apache.maven.project.DefaultProjectBuildingRequest;
import org.apache.maven.project.ProjectBuildingRequest;
import org.apache.maven.properties.internal.SystemProperties;

import org.apache.maven.artifact.resolver.filter.ExclusionArtifactFilter;
import org.apache.maven.artifact.versioning.InvalidVersionSpecificationException;
import org.apache.maven.artifact.versioning.VersionRange;
import org.apache.maven.model.Dependency;
import org.apache.maven.project.MavenProject;
import org.apache.maven.project.artifact.InvalidDependencyVersionException;

/**

Но если у вас уже есть набор dataclasses, это полезный приём — задействовать их для веб-API на FastAPI. 🤓

///

## Dataclasses в `response_model` { #dataclasses-in-response-model }

Вы также можете использовать `dataclasses` в параметре `response_model`:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

Этот dataclass будет автоматически преобразован в Pydantic dataclass.

      <item index="4" class="java.lang.String" itemvalue="org.gradle.internal.scan.UsedByScanPlugin" />
      <item index="5" class="java.lang.String" itemvalue="org.gradle.api.model.ManagedObjectCreator" />
    </list>
    <pattern value="org.gradle.internal.service.ServiceRegistrationProvider" hierarchically="true" method="configure" />
  </component>

 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package gradlebuild.docs.dsl.source.model;

import org.gradle.api.Action;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import java.util.Objects;

/**

* callback hell: callback hell (clarify as `ад обратных вызовов`)
* on the fly: на лету
* scratch the surface: поверхностно ознакомиться
* tip: совет (or `подсказка` depending on context)
* Pydantic model: Pydantic-модель (`модель Pydantic` and `Pydantic модель` are also fine)
* declare: объявить
* have the next best performance, after: быть на следующем месте по производительности после

* serialização de dados
* documentação de dados, etc.

Isso funciona da mesma forma que com os modelos Pydantic. E na verdade é alcançado da mesma maneira por baixo dos panos, usando Pydantic.

/// info | Informação

Lembre-se de que dataclasses não podem fazer tudo o que os modelos Pydantic podem fazer.

Então, você ainda pode precisar usar modelos Pydantic.