/**
 * Strategy for applying Maven inference optimizations.
 * For 4.0.0 models: applies limited inference (parent-related only).
 * For 4.1.0+ models: applies full inference optimizations.
 * Removes redundant information that can be inferred by Maven during model building.
 */
@Named
@Singleton
@Priority(30)
public class InferenceStrategy extends AbstractUpgradeStrategy {

      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.1.0</version>
          <basedir>${project.basedir}/../../api/maven-api-toolchain</basedir>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <models>
            <model>src/main/mdo/toolchains.mdo</model>
          </models>
        </configuration>

 * The maven super model

 * how parent poms are processed

 * the order in which elements in the POM are overridden

 * minimum element-set for a valid project pom


Maven super model

 Inheritance is recursive in Maven but there is a special model which is the implicit super parent in the lineage
 of models you may specify:

from fastapi.openapi.models import SecurityBase as SecurityBaseModel


class SecurityBase:
    model: SecurityBaseModel

     *
     * @param modelId The identifier of the desired raw model, must not be {@code null}.
     * @return The raw model or {@code null} if the specified model id does not refer to a known model.
     */
    Model getRawModel(String modelId);

    /**
     * Gets the profiles from the specified model that were active during model building. The model identifier should be

Vamos corrigir essas coisas adicionando alguns **modelos extras**. Aqui é onde o SQLModel vai brilhar. ✨

### Criar Múltiplos Modelos { #create-multiple-models }

No **SQLModel**, qualquer classe de modelo que tenha `table=True` é um **modelo de tabela**.

E qualquer classe de modelo que não tenha `table=True` é um **modelo de dados**, esses são na verdade apenas modelos Pydantic (com alguns recursos extras pequenos). 🤓

      <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>

Tatsächlich gibt es in einigen Fällen sogar **zwei JSON-Schemas** in OpenAPI für dasselbe Pydantic-Modell, für Eingabe und Ausgabe, je nachdem, ob sie **Defaultwerte** haben.

Sehen wir uns an, wie das funktioniert und wie Sie es bei Bedarf ändern können.

## Pydantic-Modelle für Eingabe und Ausgabe { #pydantic-models-for-input-and-output }

Nehmen wir an, Sie haben ein Pydantic-Modell mit Defaultwerten wie dieses:

    disabled = user_dict["disabled"],
    hashed_password = user_dict["hashed_password"],
)
```

/// info

For a more complete explanation of `**user_dict` check back in [the documentation for **Extra Models**](../extra-models.md#about-user-in-dict){.internal-link target=_blank}.

///

## Return the token { #return-the-token }

The response of the `token` endpoint must be a JSON object.

    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):