* Traiter des données :
    * Par exemple, si vous recevez un fichier qui doit passer par un traitement lent, vous pouvez retourner une réponse «Accepted» (HTTP 202) puis faire le traitement en arrière-plan.


## Utiliser `BackgroundTasks`

* Processing data:
    * For example, let's say you receive a file that must go through a slow process, you can return a response of "Accepted" (HTTP 202) and process the file in the background.

## Using `BackgroundTasks` { #using-backgroundtasks }

time="2020-07-12T20:45:50Z" level=info msg="config connector: mock"
time="2020-07-12T20:45:50Z" level=info msg="config connector: local passwords enabled"
time="2020-07-12T20:45:50Z" level=info msg="config response types accepted: [code token id_token]"
time="2020-07-12T20:45:50Z" level=info msg="config using password grant connector: local"
time="2020-07-12T20:45:50Z" level=info msg="config signing keys expire after: 3h0m0s"

* Обработка данных:
    * К примеру, если вы получаете файл, который должен пройти через медленный процесс, вы можете отправить ответ "Accepted" (HTTP 202) и отправить работу с файлом в фон.

## Использование класса `BackgroundTasks`

Сначала импортируйте `BackgroundTasks`, потом добавьте в функцию параметр с типом `BackgroundTasks`:

	JMP	(AX)(AX*4)
	JCS	2(PC)
	JMP	R13

// LTYPEN spec4	{ outcode($1, &$2); }
	NOP
	NOP	AX
	NOP	foo+4(SB)

// LTYPES spec5	{ outcode($1, &$2); }
	SHLL	CX, R12
	SHLL	CX, foo+4(SB)
	// Old syntax, still accepted:
	SHLL	CX, R11:AX // SHLL CX, AX, R11

// LTYPEM spec6	{ outcode($1, &$2); }
	MOVL	AX, R11
	MOVL	$4, R11
//	MOVL	AX, 0(AX):DS // no longer works - did it ever?

// LTYPEI spec7	{ outcode($1, &$2); }

이러한 작업에는 다음이 포함됩니다.

* 작업을 수행한 후 전송되는 이메일 알림
    * 이메일 서버에 연결하고 이메일을 전송하는 것은 (몇 초 정도) "느린" 경향이 있으므로, 응답은 즉시 반환하고 이메일 알림은 백그라운드에서 전송하는 게 가능합니다.
* 데이터 처리:
    * 예를 들어 처리에 오랜 시간이 걸리는 데이터를 받았을 때 "Accepted" (HTTP 202)을 반환하고, 백그라운드에서 데이터를 처리할 수 있습니다.

## `백그라운드 작업` 사용

먼저 아래와 같이 `BackgroundTasks`를 임포트하고, `BackgroundTasks`를 _경로 작동 함수_ 에서 매개변수로 가져오고 정의합니다.

{* ../../docs_src/background_tasks/tutorial001.py hl[1,13] *}

Follow either of the two links above to access the appropriate CLA and instructions for how to sign and return it. Once we receive it, we'll be able to accept your pull requests.

***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository.

### Contributing code

If you have improvements to TensorFlow, send us your pull requests! For those

discussion, and please direct specific questions to
[Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).**

The TensorFlow project strives to abide by generally accepted best practices in
open-source software development.

## Patching guidelines

Follow these steps to patch a specific version of TensorFlow, for example, to
apply fixes to bugs or security vulnerabilities:

* Before the PR is applied, the new tests should fail.
* After applying the PR, the new tests should pass.
* Coverage should stay at 100%.
* If you see the PR makes sense, or we discussed it and considered it should be accepted, you can add commits on top of the PR to tweak it, to add docs, tests, format, refactor, remove extra files, etc.
* Feel free to comment in the PR to ask for more information, to suggest changes, etc.

To set the output format, set the request `Content-Type` to the desired format. Accepted values are `application/json` for JSON output or `text/plain` for a Markdown-formatted table. The default is Markdown.

For example, the following returns a list of all available bucket metrics:
```