}

    public MaxCodePoint(String userFriendly) {
      value = decode(userFriendly);
    }
  }

  /**
   * The default values of maxCodePoint below provide pretty good performance models of different
   * kinds of common human text.
   *
   * @see MaxCodePoint#decode
   */
  @Param({"0x80", "0x90", "0x100", "0x800", "0x10000", "0x10ffff"})
  MaxCodePoint maxCodePoint;

  @Param({"16384"})

/**
 * Provides access to the contents of a source independently of the
 * backing store (e.g. file system, database, memory).
 * <p>
 * This is mainly used to parse files into objects such as Maven projects,
 * models, settings, or toolchains. The source implementation handles
 * all the details of accessing the underlying content while providing
 * a uniform API to consumers.
 * <p>
 * Sources can represent:
 * <ul>

    Undefined,
    get_compat_model_name_map,
    get_definitions,
    get_schema_from_model_field,
    lenient_issubclass,
)
from fastapi.datastructures import DefaultPlaceholder
from fastapi.dependencies.models import Dependant
from fastapi.dependencies.utils import (
    _get_flat_fields_from_params,
    get_flat_dependant,
    get_flat_params,
    get_validation_alias,
)
from fastapi.encoders import jsonable_encoder

{* ../../docs_src/body/tutorial001.py hl[4] *}

## Créez votre modèle de données

Déclarez ensuite votre modèle de données en tant que classe qui hérite de `BaseModel`.

Utilisez les types Python standard pour tous les attributs :

{* ../../docs_src/body/tutorial001.py hl[7:11] *}

Zu diesem Zeitpunkt liegen uns die Benutzerdaten aus unserer Datenbank vor, das Passwort haben wir jedoch noch nicht überprüft.

Lassen Sie uns diese Daten zunächst in das Pydantic-Modell `UserInDB` einfügen.

Sie sollten niemals Klartext-Passwörter speichern, daher verwenden wir ein (gefaktes) Passwort-Hashing-System.

Wenn die Passwörter nicht übereinstimmen, geben wir denselben Fehler zurück.

Es wird von Pydantic **nicht unterstützt**, dass ein Pydantic‑v2‑Modell Felder hat, die als Pydantic‑v1‑Modelle definiert sind, und umgekehrt.

```mermaid
graph TB
    subgraph "❌ Nicht unterstützt"
        direction TB
        subgraph V2["Pydantic-v2-Modell"]
            V1Field["Pydantic-v1-Modell"]
        end
        subgraph V1["Pydantic-v1-Modell"]
            V2Field["Pydantic-v2-Modell"]
        end
    end

{* ../../docs_src/first_steps/tutorial001_py39.py hl[8] *}

You can return a `dict`, `list`, singular values as `str`, `int`, etc.

You can also return Pydantic models (you'll see more about that later).

There are many other objects and models that will be automatically converted to JSON (including ORMs, etc). Try using your favorite ones, it's highly probable that they are already supported.

from typing import Annotated, Any, Optional, Union, cast

from annotated_doc import Doc
from fastapi.exceptions import HTTPException
from fastapi.openapi.models import OAuth2 as OAuth2Model
from fastapi.openapi.models import OAuthFlows as OAuthFlowsModel
from fastapi.param_functions import Form
from fastapi.security.base import SecurityBase
from fastapi.security.utils import get_authorization_scheme_param
from starlette.requests import Request

              <!--              <exclude>org.apache.maven.model.inheritance.DefaultInheritanceAssembler</exclude>-->

    public Model getModel() {
        return model;
    }

    /**
     * Sets the model being wrapped.
     *
     * @param model The model, may be {@code null}.
     */
    public void setModel(Model model) {
        this.model = model;
    }

    /**
     * Gets the raw model being wrapped.
     *
     * @return The raw model or {@code null} if not set.
     */
    public Model getRawModel() {