*   `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`,

@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

will be updated with the next // count value. The length of dst must be greater than or equal to the length // of src. func counterCrypt(g *GCM, dst, src []byte, cnt *[gcmBlockSize]byte) { // Copying src into a buffer improves performance on some models when // src and dst point to the same underlying array. We also need a // buffer for counter values. var ctrbuf, srcbuf [2048]byte for len(src) >= 16 { siz := len(src) if len(src) > len(ctrbuf) { siz = len(ctrbuf) } siz &^= 0xf // align siz to 16-bytes...

will be updated with the next // count value. The length of dst must be greater than or equal to the length // of src. func counterCrypt(g *GCM, dst, src []byte, cnt *[gcmBlockSize]byte) { // Copying src into a buffer improves performance on some models when // src and dst point to the same underlying array. We also need a // buffer for counter values. var ctrbuf, srcbuf [2048]byte for len(src) >= 16 { siz := len(src) if len(src) > len(ctrbuf) { siz = len(ctrbuf) } siz &^= 0xf // align siz to 16-bytes...