Starlette (e **FastAPI**) são baseados no <a href="https://anyio.readthedocs.io/en/stable/" class="external-link" target="_blank">AnyIO</a>, o que o torna compatível com ambos o <a href="https://docs.python.org/3/library/asyncio-task.html" class="external-link" target="_blank">asyncio</a> da biblioteca padrão do Python, e o <a href="https://trio.readthedocs.io/en/stable/" class="external-link" target="_blank">Trio</a>.

        print("hello World")
        ```
        »»»

"HTML element"

    a HTML opening tag – e.g. «<div>» – and a HTML closing tag – e.g. «</div>» – surrounding text or other HTML elements.


### Your task

Translate an English text – the original content – to a target language.

The original content is written in Markdown, write the translation in Markdown as well.

### 당신만의 비동기 코드 작성하기

Starlette(그리고 FastAPI)는 <a href="https://anyio.readthedocs.io/en/stable/" class="external-link" target="_blank">AnyIO</a>를 기반으로 하고있고, 따라서 파이썬 표준 라이브러리인 <a href="https://docs.python.org/3/library/asyncio-task.html" class="external-link" target="_blank">asyncio</a> 및 <a href="https://trio.readthedocs.io/en/stable/" class="external-link" target="_blank">Trio</a>와 호환됩니다.

// A Server instance encapsulates a set of devices and a Session target that
// can participate in distributed training. A server belongs to a cluster
// (specified by a ClusterSpec), and corresponds to a particular task in a
// named job. The server can communicate with any other server in the same
// cluster.

// In-process TensorFlow server.
typedef struct TF_Server TF_Server;

The sequence of execution before FastAPI 0.106.0 was like this diagram:

                # except HTTPException inside a dependency with yield.
                # This needs to happen after user middlewares because those create a
                # new contextvars context copy by using a new AnyIO task group.
                # This AsyncExitStack preserves the context for contextvars, not
                # strictly necessary for closing files but it was one of the original
                # intentions.

        `tf.distribute.InputOptions` for specific options when used with
        distribution.
    *   `tf.keras.experimental.SidecarEvaluator` is now available for a program
        intended to be run on an evaluator task, which is commonly used to
        supplement a training cluster running with
        `tf.distribute.experimental.ParameterServerStrategy` (see
        `https://www.tensorflow.org/tutorials/distribute/parameter_server_training).