}

    protected void registerModifiedProperty(String propertyName) {
        __modifiedProperties.addPropertyName(propertyName);
        registerSpecifiedProperty(propertyName); // synchronize if exists, basically for user's manual call
    }

    public void modifiedToSpecified() {
        if (__modifiedProperties.isEmpty()) {
            return; // basically no way when called in Framework (because called when SpecifyColumn exists)

Nehmen wir zum Beispiel an, Sie haben vier API-Endpunkte (*Pfadoperationen*):

* `/items/public/`
* `/items/private/`
* `/users/{user_id}/activate`
* `/items/pro/`

Dann könnten Sie für jeden davon unterschiedliche Berechtigungsanforderungen hinzufügen, nur mit Abhängigkeiten und Unterabhängigkeiten:

```mermaid
graph TB

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

That can then be used like:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",

# Declare uma variável como str
# e obtenha suporte do editor dentro da função
def main(user_id: str):
    return user_id


# Um modelo do Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Que então pode ser usado como:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",

### `**user_in.model_dump()` 에 대하여 { #about-user-in-model-dump }

#### Pydantic의 `.model_dump()` { #pydantics-model-dump }

`user_in`은 Pydantic 모델 클래스인 `UserIn`입니다.

Pydantic 모델은 모델 데이터를 포함한 `dict`를 반환하는 `.model_dump()` 메서드를 제공합니다.

따라서, 다음과 같이 Pydantic 객체 `user_in`을 생성할 수 있습니다:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

```Python
from datetime import date

from pydantic import BaseModel

# 将变量声明为 str
# 并在函数内获得编辑器支持
def main(user_id: str):
    return user_id


# 一个 Pydantic 模型
class User(BaseModel):
    id: int
    name: str
    joined: date
```

可以像这样来使用：

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",

from datetime import date

from pydantic import BaseModel

# 宣告一個變數為 string
# 並在函式中獲得 editor support
def main(user_id: str):
    return user_id


# 宣告一個 Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

可以像這樣來使用：

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",

Par exemple, supposons que vous ayez 4 endpoints d’API (chemins d’accès) :

* `/items/public/`
* `/items/private/`
* `/users/{user_id}/activate`
* `/items/pro/`

alors vous pourriez ajouter différentes exigences d’autorisations pour chacun d’eux uniquement avec des dépendances et des sous-dépendances :

```mermaid
graph TB

* `/items/private/`
* `/users/{user_id}/activate`
* `/items/pro/`

그 다음 각각에 대해 그저 의존성과 하위 의존성을 사용하여 다른 권한 요구 사항을 추가할 수 있을 겁니다:

```mermaid
graph TB

current_user(["current_user"])
active_user(["active_user"])
admin_user(["admin_user"])
paying_user(["paying_user"])

public["/items/public/"]
private["/items/private/"]
activate_user["/users/{user_id}/activate"]
pro_items["/items/pro/"]

    }

    /** Logger for this class */
    private static final Logger logger = LogManager.getLogger(QueryHelper.class);

    /** Constant used to indicate that query-based preference should be used for search routing */
    protected static final String PREFERENCE_QUERY = "_query";

    /** Prefix used to identify sort parameters in search queries */
    protected String sortPrefix = "sort:";