*   `Model.fit_generator`, `Model.evaluate_generator`,
        `Model.predict_generator`, `Model.train_on_batch`,
        `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect
        the `run_eagerly` property, and will correctly run using `tf.function`
        by default. Note that `Model.fit_generator`, `Model.evaluate_generator`,

    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):

    page.get_by_role("button", name="Try it out").click()
    page.get_by_role("heading", name="Servers").click()
    # Manually add the screenshot
    page.screenshot(path="docs/en/docs/img/tutorial/query-param-models/image01.png")

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


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

  specific language governing permissions and limitations
  under the License.

-->
<model xmlns="http://codehaus-plexus.github.io/MODELLO/2.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://codehaus-plexus.github.io/MODELLO/2.0.0 https://codehaus-plexus.github.io/modello/xsd/modello-2.0.0.xsd"
  xml.namespace="http://maven.apache.org/TOOLCHAINS/${version}"

~~ under the License.

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

Super POM

 All models implicitly inherit from a super-POM:

No está soportado por Pydantic tener un modelo de Pydantic v2 con sus propios campos definidos como modelos de Pydantic v1 o viceversa.

```mermaid
graph TB
    subgraph "❌ Not Supported"
        direction TB
        subgraph V2["Pydantic v2 Model"]
            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models { #extra-json-schema-data-in-pydantic-models }

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

That extra info will be added as-is to the output **JSON Schema** for that model, and it will be used in the API docs.

## ドキュメントを見る

自動ドキュメントを見ると、入力モデルと出力モデルがそれぞれ独自のJSON Schemaを持っていることが確認できます。

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

そして、両方のモデルは、対話型のAPIドキュメントに使用されます:

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## レスポンスモデルのエンコーディングパラメータ

レスポンスモデルにはデフォルト値を設定することができます:

{* ../../docs_src/response_model/tutorial004.py hl[11,13,14] *}

@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
    return filter_query
```

Read the new docs: [Query Parameter Models](https://fastapi.tiangolo.com/tutorial/query-param-models/).

#### `Header` Parameter Models

Use Pydantic models for `Header` parameters:

```python
from typing import Annotated

from fastapi import FastAPI, Header
from pydantic import BaseModel

import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.model.Model;

/**
 * Provides the super POM that all models implicitly inherit from.
 *
 * @since 4.0.0
 */
@Experimental
public interface SuperPomProvider extends Service {

    /**
     * Gets the super POM for the specified model version.
     *
     * @param version The model version to retrieve the super POM for (e.g. "4.0.0"), must not be {@code null}.