Ou você pode ter qualquer outra forma de comunicar com o endpoint WebSocket.

---

Mas para este exemplo, usaremos um documento HTML muito simples com algum JavaScript, tudo dentro de uma string longa.

Esse, é claro, não é o ideal e você não o usaria para produção.

Na produção, você teria uma das opções acima.

### Errors

XML error response for this API is similar to [AWS STS AssumeRoleWithWebIdentity](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html#API_AssumeRoleWithWebIdentity_Errors)

## Sample `POST` Request

```

					return ObjectLocked{}
				}

				if !ret.RetainUntilDate.Before(t) {
					return ObjectLocked{}
				}
				return nil
			}
			// https://docs.aws.amazon.com/AmazonS3/latest/dev/object-lock-overview.html#object-lock-retention-modes
			// If you try to delete objects protected by governance mode and have s3:BypassGovernanceRetention, the operation will succeed.

* 업로드 된 파일의 메타데이터를 얻을 수 있습니다.
* <a href="https://docs.python.org/3/glossary.html#term-file-like-object" class="external-link" target="_blank">file-like</a>  `async` 인터페이스를 갖고 있습니다.
* file-like object를 필요로하는 다른 라이브러리에 직접적으로 전달할 수 있는 파이썬 <a href="https://docs.python.org/3/library/tempfile.html#tempfile.SpooledTemporaryFile" class="external-link" target="_blank">`SpooledTemporaryFile`</a> 객체를 반환합니다.

### `UploadFile`

libcurand-12-1
libcusolver-dev-12-1
libcusparse-dev-12-1
libcublas-12-1
libcublas-dev-12-1
libnccl-dev=2.18.3-1+cuda12.1
libnccl2=2.18.3-1+cuda12.1
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.8.0.87-1

nvidia-cuda-nvrtc-cu12>=12.5.82,<13.0
nvidia-cuda-runtime-cu12>=12.5.82,<13.0
# The upper bound is set for the CUDNN API compatibility.
# See
# https://docs.nvidia.com/deeplearning/cudnn/backend/latest/developer/forward-compatibility.html#cudnn-api-compatibility
nvidia-cudnn-cu12>=9.3.0.75,<10.0
nvidia-cufft-cu12>=11.2.3.61,<12.0
nvidia-curand-cu12>=10.3.6.82,<11.0
nvidia-cusolver-cu12>=11.6.3.83,<12.0
nvidia-cusparse-cu12>=12.5.1.3,<13.0

libcurand-12-3
libcusolver-dev-12-3
libcusparse-dev-12-3
libcublas-12-3
libcublas-dev-12-3
libnccl-dev=2.19.3-1+cuda12.3
libnccl2=2.19.3-1+cuda12.3
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.1.1.17-1

   * Futures#addCallback addCallback()}.
   *
   * <p>Memory consistency effects: Actions in a thread prior to adding a listener <a
   * href="https://docs.oracle.com/javase/specs/jls/se7/html/jls-17.html#jls-17.4.5">
   * <i>happen-before</i></a> its execution begins, perhaps in another thread.
   *
   * <p>Guava implementations of {@code ListenableFuture} promptly release references to listeners

   * Futures#addCallback addCallback()}.
   *
   * <p>Memory consistency effects: Actions in a thread prior to adding a listener <a
   * href="https://docs.oracle.com/javase/specs/jls/se7/html/jls-17.html#jls-17.4.5">
   * <i>happen-before</i></a> its execution begins, perhaps in another thread.
   *
   * <p>Guava implementations of {@code ListenableFuture} promptly release references to listeners

 *
 * The current implementation does not address any of:
 *  - Async DNS split by IP class
 *  - Stateful handling of connectivity results
 *  - The prioritisation of addresses
 *
 * https://datatracker.ietf.org/doc/html/rfc8305#section-4
 */
internal fun reorderForHappyEyeballs(addresses: List<InetAddress>): List<InetAddress> {
  if (addresses.size < 2) {
    return addresses
  }