*   `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`,

  <mime-type type="model/vnd.flatland.3dml"/>
  <mime-type type="model/vnd.gdl">
    <glob pattern="*.gdl"/>
  </mime-type>
  <mime-type type="model/vnd.gs-gdl"/>
  <mime-type type="model/vnd.gs.gdl"/>
  <mime-type type="model/vnd.gtw">
    <glob pattern="*.gtw"/>
  </mime-type>
  <mime-type type="model/vnd.moml+xml"/>
  <mime-type type="model/vnd.mts">
    <glob pattern="*.mts"/>
  </mime-type>

@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