*/
package jcifs.pac;

/**
 * Constants for PAC (Privilege Attribute Certificate) data structures.
 * Defines buffer types and other constants used in PAC parsing and validation.
 */
public interface PacConstants {

    /**
     * PAC structure version number.
     */
    int PAC_VERSION = 0;

    /**
     * Buffer type for user logon information.
     */

 */
package jcifs.pac;

import jcifs.CIFSException;

/**
 * Exception thrown when PAC (Privilege Attribute Certificate) data cannot be decoded.
 * Indicates malformed or invalid PAC structures in Kerberos tickets.
 */
public class PACDecodingException extends CIFSException {

    private static final long serialVersionUID = 1L;

    /**

A execution command of Generate task
which generates classes corresponding your tables,
for example, entities, condition-beans to specified
directories by DBFlute properties on "dfprop" directory.

Generated structures (directories and classes) are like this:
/- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
allcommon : classes bridging to DBFlute Runtime
bsbhv     : base behaviors
bsentity  : base entities

 */
package jcifs.internal.fscc;

import jcifs.Decodable;
import jcifs.Encodable;

/**
 * Base interface for File System Control Code (FSCC) file information structures.
 * Defines common functionality for various SMB2/SMB3 file information classes used in
 * query and set file information operations, with constants for information levels.
 *
 * @author mbechler
 */

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses en Estructuras de Datos Anidadas { #dataclasses-in-nested-data-structures }

También puedes combinar `dataclasses` con otras anotaciones de tipos para crear estructuras de datos anidadas.

well defined data structure. To understand `xl.meta` here are the few things to start with

`xl.meta` carries first 8 bytes an XL header which describes the current format and the format version,
allowing the unmarshaller's to automatically use the right data structures to parse the subsequent content in the stream.

### v1.0

| Entry     | Encoding    | Content

    Arrays.fill(result, oldSize, newSize, ABSENT);
    return result;
  }

  @Override
  public int size() {
    return size;
  }

  /**
   * Ensures that all of the internal structures in the HashBiMap are ready for this many elements.
   */
  private void ensureCapacity(int minCapacity) {
    if (nextInBucketKToV.length < minCapacity) {
      int oldCapacity = nextInBucketKToV.length;

    * Parce que les structures de données de pydantic consistent seulement en une instance de classe que vous définissez; l'auto-complétion, le linting, mypy et votre intuition devrait être largement suffisante pour valider vos données.
* Valide les **structures complexes**:
    * Utilise les modèles hiérarchique de Pydantic, le `typage` Python pour les `Lists`, `Dict`, etc.

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.

    /**
     * In case of complex XML structures, combining can be done based on id.
     */
    @Deprecated(since = "4.0.0", forRemoval = true)
    String ID_COMBINATION_MODE_ATTRIBUTE = XmlService.ID_COMBINATION_MODE_ATTRIBUTE;

    /**
     * In case of complex XML structures, combining can be done based on keys.
     * This is a comma separated list of attribute names.
     */