ResponseData responseData3 = new ResponseData();
        responseData3.setHttpStatusCode(500);
        assertFalse(rule.match(responseData3));
    }

    /**
     * Test ConfigurableRule with multiple conditions (AND logic)
     */
    public void test_configurableRule_multipleConditions() {
        ConfigurableRule rule = new ConfigurableRule();
        rule.setRuleId("multiRule");

The webhooks that you define will end up in the **OpenAPI** schema and the automatic **docs UI**.

/// info

The `app.webhooks` object is actually just an `APIRouter`, the same type you would use when structuring your app with multiple files.

///

read_query["/items/"]

query_extractor --> query_or_cookie_extractor --> read_query
```

## Использование одной и той же зависимости несколько раз { #using-the-same-dependency-multiple-times }

				</DelMarkerExpiration>
                            <Status>Enabled</Status>
	                    </Rule>`,
			expectedErr: errInvalidRuleDelMarkerExpiration,
		},
		{
			inputXML: `<Rule>
				<ID>Rule with multiple tags and DelMarkerExpiration</ID>
				<Filter><And>
				<Tag><Key>k1</Key><Value>v1</Value></Tag>
				<Tag><Key>k2</Key><Value>v2</Value></Tag>
				</And></Filter>
				<DelMarkerExpiration>
					<Days>365</Days>

in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs...

import org.codelibs.core.stream.StreamUtil.StreamOf;
import org.codelibs.fess.auth.chain.AuthenticationChain;
import org.codelibs.fess.opensearch.user.exentity.User;

/**
 * Manages authentication operations across multiple authentication chains.
 * This class coordinates user operations across different authentication providers.
 */
public class AuthenticationManager {
    /** Array of authentication chains to process user operations. */

PersistentVolumeClaim.
  * Run multiple schedulers in parallel, e.g. one or more custom schedulers
alongside the default Kubernetes scheduler, using pod annotations to select
among the schedulers for each pod. Documentation is [here](http://kubernetes.io/docs/admin/multiple-schedulers.md), design doc is [here](docs/proposals/multiple-schedulers.md).

    }

    @Test
    void testHelpLogsMessages() throws Exception {
        UpgradeContext context = createMockContext();

        help.execute(context);

        // Verify that logger.info was called multiple times
        // We can't easily verify the exact content without capturing the logger output,
        // but we can verify that the method executes without errors

   * the least significant bit zero is chosen. (In such cases, both of the nearest representable
   * values are even integers; this method returns the one that is a multiple of a greater power of
   * two.)
   *
   * @throws ArithmeticException if {@code mode} is {@link RoundingMode#UNNECESSARY} and {@code x}
   *     is not precisely representable as a {@code double}
   * @since 30.0
   */

// File operations with proper resource handling
try (InputStream input = ResourceUtil.getResourceAsStream("data.txt")) {
    String content = InputStreamUtil.getUTF8String(input);
}

// Resource traversal for processing multiple files
ResourceTraversalUtil.forEach("META-INF", (resource, is) -> {
    // Process each resource in the META-INF directory
    System.out.println("Processing: " + resource);
});
```