*
     * @throws Exception
     */
    @Test
    public void testReadBytes_LargeFile() throws Exception {
        final File largeFile = tempFolder.newFile("large.dat");

        // Create a file larger than MAX_BUF_SIZE (10MB)
        // Write 11MB of data
        try (FileOutputStream fos = new FileOutputStream(largeFile)) {
            final byte[] chunk = new byte[1024 * 1024]; // 1MB

     */
    protected HashMap response_map = new HashMap(4);

    /**
     * Creates a key for the specified request for response matching.
     *
     * @param request the request to create a key for
     * @throws IOException if an I/O error occurs
     */
    protected abstract void makeKey(Request request) throws IOException;

    /**

Commencez par importer la classe `BaseModel` du module `pydantic` :

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Créer votre modèle de données { #create-your-data-model }

Déclarez ensuite votre modèle de données en tant que classe qui hérite de `BaseModel`.

Utilisez les types Python standard pour tous les attributs :

프로덕션 환경에서는 위에서 설명한 옵션 중 하나를 사용할 것입니다.

그러나 이는 WebSockets의 서버 측에 집중하고 동작하는 예제를 제공하는 가장 간단한 방법입니다:

{* ../../docs_src/websockets_/tutorial001_py310.py hl[2,6:38,41:43] *}

## `websocket` 생성하기 { #create-a-websocket }

**FastAPI** 애플리케이션에서 `websocket`을 생성합니다:

{* ../../docs_src/websockets_/tutorial001_py310.py hl[1,46:47] *}

/// note | 기술 세부사항

`from starlette.websockets import WebSocket`을 사용할 수도 있습니다.

У цьому випадку **FastAPI** ніколи не торкається `__init__` і не покладається на нього - ми використовуватимемо його безпосередньо у своєму коді.

## Створити екземпляр { #create-an-instance }

Ми можемо створити екземпляр цього класу так:

{* ../../docs_src/dependencies/tutorial011_an_py310.py hl[18] *}

      TFE_Op* op,
      const std::vector<absl::optional<std::vector<int64_t>>>& input_shapes_vec,
      const std::vector<TF_Tensor*>& input_tensors,
      const absl::optional<std::vector<int64_t>>& expected_shape) {
    // Create input_shapes.
    TF_ShapeAndTypeList* input_shapes =
        TF_NewShapeAndTypeList(input_shapes_vec.size());
    for (size_t i = 0; i < input_shapes_vec.size(); ++i) {
      const auto& input_shape = input_shapes_vec[i];

     *
     * @param accessResult The access result to store.
     */
    @Override
    public void store(final OpenSearchAccessResult accessResult) {
        super.insert(accessResult, accessResult.getId() == null ? OpType.CREATE : OpType.INDEX);
    }

    /**
     * Updates an access result in the OpenSearch index.
     *
     * @param accessResult The access result to update.
     */
    @Override

 - The default Bootstrap Token created with `kubeadm init` v1.8 expires
 and is deleted after 24 hours by default to limit the exposure of the
 valuable credential. You can create a new Bootstrap Token with
 `kubeadm token create` or make the default token permanently valid by specifying
 `--token-ttl 0` to `kubeadm init`. The default token can later be deleted with
 `kubeadm token delete`.

---

## Instalar FastAPI { #install-fastapi }

El primer paso es instalar FastAPI.

Asegúrate de crear un [entorno virtual](../virtual-environments.md), actívalo, y luego **instala FastAPI**:

<div class="termy">

```console
$ pip install "fastapi[standard]"

---> 100%
```

</div>

/// note | Nota

    /**
     * Open for reading and writing.
     */
    int O_RDWR = 0x03;
    /**
     * Open in append mode.
     */
    int O_APPEND = 0x04;

    // Open Function Encoding
    /**
     * Create file if it does not exist.
     */
    int O_CREAT = 0x0010;
    /**
     * Fail if the file exists.
     */
    int O_EXCL = 0x0020;
    /**
     * Truncate file if it exists.
     */