mkdocs-material==9.5.18
mdx-include >=1.4.1,<2.0.0
mkdocs-redirects>=1.2.1,<1.3.0
typer >=0.12.0
pyyaml >=5.3.1,<7.0.0
# For Material for MkDocs, Chinese search
jieba==0.42.1
# For image processing by Material for MkDocs
pillow==10.3.0
# For image processing by Material for MkDocs
cairosvg==2.7.0
mkdocstrings[python]==0.24.3
griffe-typingdoc==0.2.2
# For griffe, it formats with black
black==24.3.0

Doing this, our `GzipRequest` will take care of decompressing the data (if necessary) before passing it to our *path operations*.

After that, all of the processing logic is the same.

But because of our changes in `GzipRequest.body`, the request body will be automatically decompressed when it is loaded by **FastAPI** when needed.

## Accessing the request body in an exception handler

    logging.info(
        f"Sleeping for {sleep_time} seconds to avoid "
        "race conditions and multiple comments"
    )
    time.sleep(sleep_time)

    # Get PR
    logging.debug(f"Processing PR: #{github_event.pull_request.number}")
    pr = repo.get_pull(github_event.pull_request.number)
    label_strs = {label.name for label in pr.get_labels()}
    langs = []
    for label in label_strs:

---

Common examples of CPU bound operations are things that require complex math processing.

For example:

* **Audio** or **image processing**.

!!! tip
    If you use Python 3.9 or above, you don't have to import `List` from `typing`, you can use the same regular `list` type instead.

By doing that, your editor can provide support even while processing items from the list:

<img src="/img/python-types/image05.png">

Without types, that's almost impossible to achieve.

Notice that the variable `item` is one of the elements in the list `items`.

Et comme la plupart du temps d'exécution est pris par du "vrai" travail (et non de l'attente), et que le travail dans un ordinateur est fait par un <abbr title="Central Processing Unit">CPU</abbr>, ce sont des problèmes dits "CPU bound".

---

Des exemples communs d'opérations "CPU bounds" sont les procédés qui requièrent des traitements mathématiques complexes.

Par exemple :

    * As connecting to an email server and sending an email tends to be "slow" (several seconds), you can return the response right away and send the email notification in the background.
* 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 it in the background.

## Using `BackgroundTasks`

このシナリオでは、清掃員 (あなたを含む) のそれぞれがプロセッサとなり、それぞれの役割を果たします。

また、実行時間のほとんどは (待機ではなく) 実際の作業に費やされ、コンピュータでの作業は<abbr title="Central Processing Unit">CPU</abbr>によって行われます。これらの問題は「CPUバウンド」と言います。

---

CPUバウンド操作の一般的な例は、複雑な数学処理が必要なものです。

例えば:

* **オーディオ** や **画像処理**。

!!! tip
    As FastAPI is an async framework, one request could start being processed, and before finishing, another request could be received and start processing as well, and it all could be processed in the same thread.

That way, you don't have to read it all first in memory, and you can pass that generator function to the `StreamingResponse`, and return it.

This includes many libraries to interact with cloud storage, video processing, and others.

```{ .python .annotate hl_lines="2  10-12  14" }
{!../../../docs_src/custom_response/tutorial008.py!}
```