* Fügen Sie diese in ein Pydantic-Modell ein.
* Erzeugen Sie aus dem empfangenen Modell ein `dict` ohne Defaultwerte (mittels `exclude_unset`).
    * So ersetzen Sie nur die tatsächlich vom Benutzer gesetzten Werte, statt dass bereits gespeicherte Werte mit Defaultwerten des Modells überschrieben werden.

/**
 * A test which demonstrates maven's recursive inheritance where
 * a distinct value is taken from each parent contributing to
 * the final model of the project being assembled. There is no
 * overriding going on amongst the models being used in this test:
 * each model in the lineage is providing a value that is not present
 * anywhere else in the lineage. We are just making sure that values

但本节以 `PATCH` 为例的原因是，该操作就是为了这种用例创建的。

///

/// note | 笔记

注意，输入模型仍需验证。

因此，如果希望接收的部分更新数据可以省略其他所有属性，则要把模型中所有的属性标记为可选（使用默认值或 `None`）。

为了区分用于**更新**所有可选值的模型与用于**创建**包含必选值的模型，请参照[更多模型](extra-models.md){.internal-link target=_blank} 一节中的思路。

Чтобы отличить модели со всеми необязательными значениями для **обновления** от моделей с обязательными значениями для **создания**, можно воспользоваться идеями, описанными в [Дополнительные модели](extra-models.md){.internal-link target=_blank}.

    }

    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>

        }

        return false;
    }

    /**
     * Checks if a model version is eligible for inference optimizations.
     * Models 4.0.0+ are eligible (4.0.0 has limited inference, 4.1.0+ has full inference).
     *
     * @param modelVersion the model version to check
     * @return true if eligible for inference
     */

Importe `BaseSettings` do Pydantic e crie uma subclasse, muito parecido com um modelo do Pydantic.

Da mesma forma que com modelos do Pydantic, você declara atributos de classe com anotações de tipo e, possivelmente, valores padrão.

Você pode usar as mesmas funcionalidades e ferramentas de validação que usa em modelos do Pydantic, como diferentes tipos de dados e validações adicionais com `Field()`.

//// tab | Pydantic v2

    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

Denken Sie daran, dass `Query`, `Path` und andere, wenn Sie sie von `fastapi` importieren, tatsächlich Funktionen sind, die spezielle Klassen zurückgeben.

///

/// tip | Tipp

Beachten Sie, wie jedes Attribut eines Modells mit einem Typ, Defaultwert und `Field` die gleiche Struktur hat wie ein Parameter einer *Pfadoperation-Funktion*, nur mit `Field` statt `Path`, `Query`, `Body`.

///