Doc(
            """
            Pydantic's `include` parameter, passed to Pydantic models to set the
            fields to include.
            """
        ),
    ] = None,
    exclude: Annotated[
        Optional[IncEx],
        Doc(
            """
            Pydantic's `exclude` parameter, passed to Pydantic models to set the
            fields to exclude.
            """
        ),
    ] = None,

    boolean isResolveDependencies();

    /**
     * Controls the level of validation to perform on processed models. By default, models are validated in strict mode.
     *
     * @param validationLevel The level of validation to perform on processed models, e.g.
     *            {@link org.apache.maven.model.building.ModelBuildingRequest#VALIDATION_LEVEL_STRICT}.
     * @return This configuration, never {@code null}.
     */

    def _model_rebuild(model: Type[BaseModel]) -> None:
        model.model_rebuild()

    def _model_dump(
        model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any
    ) -> Any:
        return model.model_dump(mode=mode, **kwargs)

    def _get_model_config(model: BaseModel) -> Any:
        return model.model_config

    def get_schema_from_model_field(

from fastapi import Depends, FastAPI, HTTPException, Request, Response
from sqlalchemy.orm import Session

from . import crud, models, schemas
from .database import SessionLocal, engine

models.Base.metadata.create_all(bind=engine)

app = FastAPI()


@app.middleware("http")
async def db_session_middleware(request: Request, call_next):
    response = Response("Internal server error", status_code=500)
    try:

`response_model` receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a `list` of Pydantic models, like `List[Item]`.

FastAPI will use this `response_model` to do all the data documentation, validation, etc. and also to **convert and filter the output data** to its type declaration.

!!! tip

     */
    private static final Map<String, Model> SUPER_MODELS = new ConcurrentHashMap<>();

    @Inject
    public DefaultSuperPomProvider(ModelProcessor modelProcessor) {
        this.modelProcessor = modelProcessor;
    }

    @Override
    public Model getSuperPom(String version) {
        return SUPER_MODELS.computeIfAbsent(Objects.requireNonNull(version), v -> readModel(version, v));
    }

    title: Inbetriebnahme eines scikit-learn-Modells mit ONNX und FastAPI
  - author: Felix Schürmeyer
    author_link: https://hellocoding.de/autor/felix-schuermeyer/
    link: https://hellocoding.de/blog/coding-language/python/fastapi
    title: REST-API Programmieren mittels Python und dem FastAPI Modul
  Japanese:
  - author: '@bee2'

    It improves editor support for Pydantic models, with:

    * auto-completion
    * type checks
    * refactoring
    * searching
    * inspections

## Use the model

Inside of the function, you can access all the attributes of the model object directly:

=== "Python 3.10+"

    ```Python hl_lines="19"

              <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
              <models>
                <model>src/main/mdo/toolchains.mdo</model>
              </models>
              <templates>
                <template>model.vm</template>
              </templates>
              <params>
                <param>packageModelV4=org.apache.maven.api.toolchain</param>
                <param>locationTracking=true</param>

from sqlalchemy.orm import Session

from . import crud, models, schemas
from .database import SessionLocal, engine

models.Base.metadata.create_all(bind=engine)

app = FastAPI()


# Dependency
def get_db():
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()


@app.post("/users/", response_model=schemas.User)