Ancak bazı durumlarda `Request` nesnesine doğrudan erişmeniz gerekebilir.

## `Request` nesnesi hakkında detaylar { #details-about-the-request-object }

 *
 * Uses the following as input:
 * <ul>
 * <li>Meta-data extracted from the source by {@link gradlebuild.docs.dsl.source.ExtractDslMetaDataTask}.</li>
 * <li>Meta-data about the plugins, in the form of an XML file.</li>
 * <li>{@code sourceFile} - A main docbook template file containing the introductory material and a list of classes to document.</li>

import org.apache.maven.plugin.MojoExecution;
import org.apache.maven.plugin.descriptor.MojoDescriptor;
import org.apache.maven.project.MavenProject;

/**
 * <p>
 * Wraps individual MojoExecutions, containing information about completion status and scheduling.
 * </p>
 * <strong>NOTE:</strong> This class is not part of any public api and can be changed or deleted without prior notice.
 *
 * @since 3.0
 */
public class ExecutionPlanItem {

// computes the ETag as MD5 of the encrypted content but there is
// no way to verify this assumption since the encryption happens
// inside AWS S3.
// Therefore, S3 clients must not make any assumption about ETags
// in case of SSE-C or SSE-KMS except that the ETag is well-formed.
//
// To put all of this into a simple rule:
//
//	SSE-S3 : ETag == MD5
//	SSE-C  : ETag != MD5
//	SSE-KMS: ETag != MD5
//

import org.apache.maven.api.model.Model;

/**
 * Collects problems that are encountered during model building. The primary purpose of this component is to account for
 * the fact that the problem reporter has/should not have information about the calling context and hence cannot provide
 * an expressive source hint for the model problem. Instead, the source hint is configured by the model builder before

---
name: TensorFlow Lite Converter Issue
about: Use this template for reporting issues during model conversion to TFLite
labels: 'TFLiteConverter'

---

### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):

### 2. Code

package org.codelibs.fess.exception;

import org.codelibs.fess.crawler.exception.CrawlingAccessException;

/**
 * Exception thrown when an error occurs during data store crawling operations.
 * This exception provides information about the URL where the error occurred
 * and whether the crawling process should be aborted.
 */
public class DataStoreCrawlingException extends CrawlingAccessException {

    private static final long serialVersionUID = 1L;

    }

    /**
     * The logger name for a component with a non-null hint is <code>role.hint</code>.
     * <b>Warning</b>: this does not conform to logger name as class name convention.
     * (and what about <code>null</code> and <code>default</code> hint equivalence?)
     */
    @Override
    public Logger getLoggerForComponent(String role, String hint) {
        return (null == hint
                ? getLoggerForComponent(role)

///

## Add extra information { #add-extra-information }

You can declare extra information in `Field`, `Query`, `Body`, etc. And it will be included in the generated JSON Schema.

You will learn more about adding extra information later in the docs, when learning to declare examples.

/// warning

Extra keys passed to `Field` will also be present in the resulting OpenAPI schema for your application.

Mais il existe des situations où vous pouvez avoir besoin d'accéder directement à l'objet `Request`.

## Détails sur l'objet `Request` { #details-about-the-request-object }