/**
 * 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
 * formed by a combination of group id, artifact id, version and tag. The first three components generally refer to the

        problems.clear();
        problems = validator.validate(model2);
        assertEquals(2, problems.size());

        repo = repo.withUrl("http://xxx.xxx.com");
        model2 = Settings.newBuilder()
                .profiles(List.of(prof.withRepositories(List.of(repo))))
                .build();
        problems.clear();
        problems = validator.validate(model2);
        assertEquals(1, problems.size());

{!../../../docs_src/body/tutorial001.py!}
```

## Crie seu modelo de dados

Então você declara seu modelo de dados como uma classe que herda `BaseModel`.

Utilize os tipos Python padrão para todos os atributos:

```Python hl_lines="7-11"
{!../../../docs_src/body/tutorial001.py!}
```

        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.0.0</version>
          <models>
            <model>src/main/mdo/profiles.mdo</model>
            <model>src/main/mdo/paramdoc.mdo</model>
          </models>
        </configuration>
        <executions>
          <execution>
            <id>modello</id>
            <goals>

 * <ol>
 * <li>success - in which case only the model field is set
 * <li>success with warnings - model field + non-error model problems
 * <li>error - no model, but diagnostics
 * <li>error - (partial) model and diagnostics
 * </ol>
 * Could encode these variants as subclasses, but kept in one for now
 *
 * @param <T> the model type
 */
public class Result<T> {

    /**

  - author: Kaustubh Gupta
    author_link: https://medium.com/@kaustubhgupta1828/
    link: https://www.analyticsvidhya.com/blog/2021/06/deploying-ml-models-as-api-using-fastapi-and-heroku/
    title: Deploying ML Models as API Using FastAPI and Heroku
  - link: https://jarmos.netlify.app/posts/using-github-actions-to-deploy-a-fastapi-project-to-heroku/

/**
 * A test which demonstrates maven's recursive inheritance where
 * a distinct value is taken from each parent contributing to
 * the final model of the project being assembled. There is no
 * overriding going on amongst the models being used in this test:
 * each model in the lineage is providing a value that is not present
 * anywhere else in the lineage. We are just making sure that values

```Python hl_lines="25-28"
{!../../../docs_src/async_sql_databases/tutorial001.py!}
```

## Create models

Create Pydantic models for:

* Notes to be created (`NoteIn`).
* Notes to be returned (`Note`).

```Python hl_lines="31-33  36-39"
{!../../../docs_src/async_sql_databases/tutorial001.py!}
```

By creating these Pydantic models, the input data will be validated, serialized (converted), and annotated (documented).

/**
 * A test which demonstrates maven's recursive inheritance where
 * a distinct value is taken from each parent contributing to
 * the final model of the project being assembled. There is no
 * overriding going on amongst the models being used in this test:
 * each model in the lineage is providing a value that is not present
 * anywhere else in the lineage. We are just making sure that values

Da mesma forma, este banco de dados não receberia um modelo Pydantic (um objeto com atributos), apenas um `dict`.

Você pode usar a função `jsonable_encoder` para resolver isso.

A função recebe um objeto, como um modelo Pydantic e retorna uma versão compatível com JSON:

=== "Python 3.10+"

    ```Python hl_lines="4  21"