final NodeList nodeList = doc.getElementsByTagName("component");
                    for (int i = 0; i < nodeList.getLength(); i++) {
                        final Node node = nodeList.item(i);
                        final NamedNodeMap attributes = node.getAttributes();
                        if (attributes != null) {
                            final Node classAttr = attributes.getNamedItem("class");

    newDocumentBuilder()
        .parse(url)
        .getElementsByTagName("version").let { versions ->
            (0 until versions.length)
                .map { idx -> (versions.item(idx) as Element).textContent }
                .reversed()
        }


internal
fun Properties.store(file: java.io.File, comment: String? = null) {

_2024-04-17_

 *  Breaking: Move coroutines extensions to okhttp3.coroutines. Previously this artifact shared the
    `okhttp3` package name with our core module, which is incompatible with the Java Platform Module
    System.

 *  Fix in okhttp-coroutines: Publish a valid artifact. The coroutines JAR file in 5.0.0-alpha.13
    was corrupt and should not be used.


## Version 5.0.0-alpha.13

_2024-04-16_

```

////

Beachten Sie, dass die *Pfadoperationen* die Modelle definieren, welche diese für die Request- und Response-<abbr title="Die eigentlichen Nutzdaten, abzüglich der Metadaten">Payload</abbr> verwenden, indem sie die Modelle `Item` und `ResponseMessage` verwenden.

### API-Dokumentation

Wenn Sie zur API-Dokumentation gehen, werden Sie sehen, dass diese die **Schemas** für die Daten enthält, welche in Requests gesendet und in Responses empfangen werden:

```

////

//// tab | Python 3.8+

```Python hl_lines="9  14  20  23  27"
{!> ../../docs_src/body_nested_models/tutorial007.py!}
```

////

/// info

请注意 `Offer` 拥有一组 `Item` 而反过来 `Item` 又是一个可选的 `Image` 列表是如何发生的。

///

## 纯列表请求体

如果你期望的 JSON 请求体的最外层是一个 JSON `array`（即 Python `list`），则可以在路径操作函数的参数中声明此类型，就像声明 Pydantic 模型一样：

```Python
images: List[Image]
```

例如：

    for (boolean[] array : arrays) {
      length += array.length;
    }
    boolean[] result = new boolean[checkNoOverflow(length)];
    int pos = 0;
    for (boolean[] array : arrays) {
      System.arraycopy(array, 0, result, pos, array.length);
      pos += array.length;
    }
    return result;
  }

  private static int checkNoOverflow(long result) {
    checkArgument(

                e = i - 4;
                break;
            }
        }

        StackTraceElement[] res = new StackTraceElement[e - s];
        System.arraycopy(stackTrace, s, res, 0, e - s);
        return res;
    }


    /**
     * {@inheritDoc}
     *
     * @see java.lang.Object#finalize()
     */
    @Override

### Criar um Engine
Um `engine` SQLModel (por baixo dos panos, ele é na verdade um `engine` do SQLAlchemy) é o que **mantém as conexões** com o banco de dados.

Você teria **um único objeto `engine`** para todo o seu código se conectar ao mesmo banco de dados.

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[14:18] hl[14:15,17:18] *}

    for (boolean[] array : arrays) {
      length += array.length;
    }
    boolean[] result = new boolean[checkNoOverflow(length)];
    int pos = 0;
    for (boolean[] array : arrays) {
      System.arraycopy(array, 0, result, pos, array.length);
      pos += array.length;
    }
    return result;
  }

  private static int checkNoOverflow(long result) {
    checkArgument(

		if t.Arn == arn {
			tgt = t.Clone()
			tgt.Credentials = t.Credentials
			return tgt
		}
	}
	return tgt
}

// NewBucketTargetSys - creates new replication system.
func NewBucketTargetSys(ctx context.Context) *BucketTargetSys {
	sys := &BucketTargetSys{
		arnRemotesMap: make(map[string]arnTarget),
		targetsMap:    make(map[string][]madmin.BucketTarget),