}

    /**
     * Setter to specify the list of annotations that allow missed documentation.
     * Only short names are allowed, e.g. {@code Generated}.
     *
     * @param userAnnotations user's value.
     */
    public void setSkipAnnotations(String... userAnnotations) {
        skipAnnotations = Arrays.asList(userAnnotations);
    }

    /**
     * Setter to specify pattern for types to ignore.
     *

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

那麼你就能只透過依賴與子依賴，為每個端點加入不同的權限需求：

```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/"]

    }

    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)

    }

    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)

# Declara una variable como un str
# y obtiene soporte del editor dentro de la función
def main(user_id: str):
    return user_id


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

Que luego puede ser usado como:

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

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

For example, let's say you have 4 API endpoints (*path operations*):

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

then you could add different permission requirements for each of them just with dependencies and sub-dependencies:

```mermaid
graph TB

current_user(["current_user"])

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",

```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",