/// info | 情報

`Offer`は`Item`のリストであり、それらがさらにオプションの`Image`のリストを持っていることに注目してください。

///

## 純粋なリストのボディ { #bodies-of-pure-lists }

期待するJSONボディのトップレベルの値がJSON`array`（Pythonの`list`）であれば、Pydanticモデルと同じように、関数のパラメータで型を宣言することができます:

```Python
images: list[Image]
```

以下のように:

{* ../../docs_src/body_nested_models/tutorial008_py310.py hl[13] *}

# (as is the case on centos-6, for example) then that OS will again be
# excluded.
debian-8
opensuse-leap-15.1
ol-7.7
sles-12.3 # older version used in Vagrant image
sles-12.5
sles-15.1
sles-15.2
sles-15.3

# These OSes are deprecated and filtered starting with 8.0.0, but need to be excluded
# for PR checks
centos-6

Une fonction à exécuter comme tâche d'arrière-plan est juste une fonction standard qui peut recevoir des paramètres.

Elle peut être une fonction asynchrone (`async def`) ou une fonction normale (`def`), **FastAPI** saura la gérer correctement.

Dans cet exemple, la fonction de tâche écrira dans un fichier (afin de simuler un envoi d'email).

	// Peek into a zip archive
	xMinIOExtract = "x-minio-extract"
)

// splitZipExtensionPath splits the S3 path to the zip file and the path inside the zip:
//
//	e.g  /path/to/archive.zip/backup-2021/myimage.png => /path/to/archive.zip, backup/myimage.png
func splitZipExtensionPath(input string) (zipPath, object string, err error) {
	idx := strings.Index(input, archivePattern)
	if idx < 0 {
		// Should never happen

            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
          docker image prune -af
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true

            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
          docker image prune -af
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true

You can create a custom sub-class of `StreamingResponse` that sets the `Content-Type` header to the type of data you're streaming.

For example, you can create a `PNGStreamingResponse` that sets the `Content-Type` header to `image/png` using the `media_type` attribute:

{* ../../docs_src/stream_data/tutorial002_py310.py ln[6,19:20] hl[20] *}

Then you can use this new class in `response_class=PNGStreamingResponse` in your *path operation function*:

* Add an entry to the salt config to allow Debian jessie on GCE. ([#25123](https://github.com/kubernetes/kubernetes/pull/25123), [@jlewi](https://github.com/jlewi))
    * As with the existing Wheezy image on GCE, docker is expected to already be installed in the image.
* Fix DeletingLoadBalancer event generation. ([#24833](https://github.com/kubernetes/kubernetes/pull/24833), [@a-robinson](https://github.com/a-robinson))

## 주요 응답에 대한 추가 미디어 타입 { #additional-media-types-for-the-main-response }

같은 `responses` 파라미터를 사용해 동일한 주요 응답에 대해 다른 미디어 타입을 추가할 수도 있습니다.

예를 들어, *경로 처리*가 JSON 객체(미디어 타입 `application/json`) 또는 PNG 이미지(미디어 타입 `image/png`)를 반환할 수 있다고 선언하기 위해 `image/png`라는 추가 미디어 타입을 추가할 수 있습니다:

{* ../../docs_src/additional_responses/tutorial002_py310.py hl[17:22,26] *}

/// note | 참고

이미지는 `FileResponse`를 사용해 직접 반환해야 한다는 점에 유의하세요.

///

video/x-m4v=m4v\n\
audio/x-aif=aif\n\
audio/midi=midi\n\
audio/mpga=mpga\n\
audio/mp4=mp4a\n\
audio/ogg=oga\n\
audio/x-wav=wav\n\
image/webp=webp\n\
image/bmp=bmp\n\
image/x-icon=ico\n\
image/x-icon=ico\n\
image/png=png\n\
image/svg+xml=svg\n\
image/tiff=tiff\n\
image/jpeg=jpg\n\

# Number of documents to process per reindex operation.
index.reindex.size=100
# Request body template for reindex operations.