// ===================================================================================
    //                                                                           Attribute
    //                                                                           =========
    @Resource
    private RelatedQueryService relatedQueryService;
    @Resource
    private RelatedQueryPager relatedQueryPager;

    // ===================================================================================
    //                                                                           Attribute
    //                                                                           =========
    /** Service for group operations. */
    @Resource
    private GroupService groupService;

import java.io.ByteArrayInputStream;
import java.io.DataInputStream;
import java.io.IOException;
import java.util.Date;

import jcifs.smb.SID;

/**
 * Contains user logon information from a PAC (Privilege Attribute Certificate).
 * This class parses and provides access to user authentication and authorization
 * data from Kerberos tickets, including user identity, group memberships, and
 * logon metadata.
 */

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

That extra info will be added as-is to the output **JSON Schema** for that model, and it will be used in the API docs.

You can use the attribute `model_config` that takes a `dict` as described in [Pydantic's docs: Configuration](https://docs.pydantic.dev/latest/api/config/).

    // ===================================================================================
    //                                                                           Attribute
    //                                                                           =========
    @Resource
    private RelatedContentService relatedContentService;
    @Resource

    // ===================================================================================
    //                                                                           Attribute
    //

    /**
     * The helper for related content.
     */
    @Resource
    protected RelatedContentHelper relatedContentHelper;

    /**
     * The helper for related queries.
     */

Wir könnten es besser machen.

Wir können ein `UserBase`-Modell deklarieren, das als Basis für unsere anderen Modelle dient. Und dann können wir Unterklassen dieses Modells erstellen, die seine Attribute (Typdeklarationen, Validierung usw.) erben.

Die ganze Datenkonvertierung, -validierung, -dokumentation usw. wird immer noch wie gewohnt funktionieren.

    // ===================================================================================
    //                                                                           Attribute
    //                                                                           =========
    @Resource
    private CharMappingService charMappingService;
    @Resource
    private CharMappingPager charMappingPager;

## Pydantic models { #pydantic-models }

[Pydantic](https://docs.pydantic.dev/) is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

And each attribute has a type.

Then you create an instance of that class with some values and it will validate the values, convert them to the appropriate type (if that's the case) and give you an object with all the data.

        document.body().html(rewritten);
    }

    private void handleVideos(Document document) {
        String rewritten = document.body().html();

        // Replace YouTube references by embedded videos, ?si= attribute is a must
        // E.g. @youtube(Summary,UN0AFCLASZA?si=9aG5tDzj6nL1_IKT&start=371)@ => https://www.youtube.com/embed/UN0AFCLASZA?si=9aG5tDzj6nL1_IKT&start=371"