@DisplayName("Should handle hexdump with offset")
    void testHexdumpWithOffset() {
        ByteArrayOutputStream baos = new ByteArrayOutputStream();
        PrintStream ps = new PrintStream(baos);

        // Create test data and dump from offset
        byte[] data = createTestData(64);
        int offset = 16;
        int length = 32;

        Hexdump.hexdump(ps, data, offset, length);
        String output = baos.toString();

    private Proxy proxy;

    private List<ArtifactRepository> mirroredRepositories = Collections.emptyList();

    private boolean blocked;

    public MavenArtifactRepository() {}

    /**
     * Create a remote download repository.
     *
     * @param id        the unique identifier of the repository
     * @param url       the URL of the repository
     * @param layout    the layout of the repository

Las variables de entorno pueden ser útiles para manejar **configuraciones** de aplicaciones, como parte de la **instalación** de Python, etc.

## Crear y Usar Variables de Entorno { #create-and-use-env-vars }

Puedes **crear** y usar variables de entorno en la **shell (terminal)**, sin necesidad de Python:

//// tab | Linux, macOS, Windows Bash

<div class="termy">

```console

* `externalDocs`: 외부 문서를 설명하는 `dict`이며:
    * `description`: 외부 문서에 대한 간단한 설명을 담은 `str`입니다.
    * `url` (**필수**): 외부 문서의 URL을 담은 `str`입니다.

### 태그에 대한 메타데이터 생성 { #create-metadata-for-tags }

`users` 및 `items`에 대한 태그 예시로 시도해 보겠습니다.

태그에 대한 메타데이터를 생성하고 이를 `openapi_tags` 매개변수로 전달하세요:

{* ../../docs_src/metadata/tutorial004_py310.py hl[3:16,18] *}

### Створіть клас `Enum` { #create-an-enum-class }

Імпортуйте `Enum` і створіть підклас, що наслідується від `str` та `Enum`.

		i--
	}

	j := i
	for j > 0 && !os.IsPathSeparator(dirPath[j-1]) { // Scan backward over element.
		j--
	}

	if j > 1 {
		// Create parent.
		if err := osMkdirAll(dirPath[:j-1], perm, baseDir); err != nil {
			return err
		}
	}

	// Parent now exists; invoke Mkdir and use its result.
	if err := Mkdir(dirPath, perm); err != nil {

export TFCI=py311,linux_x86,public_cache,disk_cache

# Recommended: Configure Docker. (Linux only)
#
#   TF uses hub.docker.com/r/tensorflow/build containers for CI,
#   and scripts on Linux create a persistent container called "tf"
#   which mounts your TensorFlow directory into the container.
#
#   Important: because the container is persistent, you cannot change TFCI

///

## プロジェクトの作成 { #create-a-project }

まず、プロジェクト用のディレクトリを作成します。

私は通常 home/user ディレクトリの中に `code` というディレクトリを用意していて、プロジェクトごとに1つのディレクトリをその中に作成しています。

<div class="termy">

```console
// Go to the home directory
$ cd
// Create a directory for all your code projects
$ mkdir code
// Enter into that code directory
$ cd code
// Create a directory for this project
$ mkdir awesome-project

        try {
            final ByteArrayOutputStream bos = new ByteArrayOutputStream();
            final ASN1OutputStream dos = ASN1OutputStream.create(bos, ASN1Encoding.DER);
            dos.writeObject(new DERSequence(mechs));
            dos.close();
            return bos.toByteArray();
        } catch (final IOException e) {

    }

    @Test
    @DisplayName("send retries on transport errors and restores request/response state")
    void send_retries_on_transportError() throws Exception {
        // Create a spy to track method calls
        SmbTreeConnection c = spy(newConn());

        // Prepare a minimal locator
        SmbResourceLocatorImpl loc = new SmbResourceLocatorImpl(ctx, smbUrl("smb://srv/share/path"));