<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>

~~ under the License.

 -----
 Super POM
 -----
 Hervé Boutemy
 -----
 2011-09-12
 -----

Super POM

 All models implicitly inherit from a super-POM:

      </plugin>
      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.2.0</version>
          <models>
            <model>../../api/maven-api-cli/src/main/mdo/core-extensions.mdo</model>
          </models>
          <templates>
            <template>reader-stax.vm</template>

*   Keras compile/fit behavior for functional and subclassed models have been
    unified. Model properties such as `metrics`, `metrics_names` will now be
    available only after **training/evaluating the model on actual data** for
    functional models. `metrics` will **now include** model `loss` and output
    losses.`loss_functions` property has been removed from the model. This was

    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):

### Create the `Settings` object

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.

//// tab | Pydantic v2

We are not restricted to having only one dependency that can return that type of data.

///

## Other models

You can now get the current user directly in the *path operation functions* and deal with the security mechanisms at the **Dependency Injection** level, using `Depends`.

      </plugin>
      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <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>

    page = context.new_page()
    page.goto("http://localhost:8000/docs")
    page.get_by_role("link", name="/items/").click()
    # Manually add the screenshot
    page.screenshot(path="docs/en/docs/img/tutorial/cookie-param-models/image01.png")

    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["fastapi", "run", "docs_src/cookie_param_models/tutorial001.py"]
)
try:

      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <basedir>${project.basedir}/../../api/maven-api-model</basedir>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <version>4.1.0</version>
          <models>
            <model>src/main/mdo/maven.mdo</model>
          </models>
          <params>