},
                    },
                }
            },
            "/tuple-of-models/": {
                "post": {
                    "summary": "Post Tuple Of Models",
                    "operationId": "post_tuple_of_models_tuple_of_models__post",
                    "requestBody": {
                        "content": {
                            "application/json": {

              <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
              <version>1.2.0</version>
              <models>
                <model>src/main/mdo/metadata.mdo</model>
              </models>
              <templates>
                <template>model.vm</template>
              </templates>
              <params>
                <param>packageModelV4=org.apache.maven.api.metadata</param>

              <version>2.0.0</version>
              <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
              <models>
                <model>src/main/mdo/settings.mdo</model>
              </models>
              <templates>
                <template>model.vm</template>
              </templates>
              <params>
                <param>packageModelV4=org.apache.maven.api.settings</param>

Um beispielsweise eine weitere Response mit dem Statuscode `404` und einem Pydantic-Modell `Message` zu deklarieren, können Sie schreiben:

```Python hl_lines="18  22"
{!../../../docs_src/additional_responses/tutorial001.py!}
```

!!! note "Hinweis"
    Beachten Sie, dass Sie die `JSONResponse` direkt zurückgeben müssen.

!!! info

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

from docs_src.events.tutorial003 import (
    app,
    fake_answer_to_everything_ml_model,
    ml_models,
)


def test_events():
    assert not ml_models, "ml_models should be empty"
    with TestClient(app) as client:
        assert ml_models["answer_to_everything"] == fake_answer_to_everything_ml_model
        response = client.get("/predict", params={"x": 2})
        assert response.status_code == 200, response.text

            if route.callbacks:
                callback_flat_models.extend(get_fields_from_routes(route.callbacks))
            params = get_flat_params(route.dependant)
            request_fields_from_routes.extend(params)

    flat_models = callback_flat_models + list(
        body_fields_from_routes + responses_from_routes + request_fields_from_routes
    )
    return flat_models


def get_openapi(
    *,
    title: str,

package schema_test

import (
	"database/sql"
	"time"

	"gorm.io/gorm"
	"gorm.io/gorm/utils/tests"
)

type User struct {
	*gorm.Model
	Name      *string
	Age       *uint
	Birthday  *time.Time
	Account   *tests.Account
	Pets      []*tests.Pet
	Toys      []*tests.Toy `gorm:"polymorphic:Owner"`
	CompanyID *int
	Company   *tests.Company
	ManagerID *uint
	Manager   *User

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)