Isso nos ajudará dentro da função com...

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
```

👈 💪 ⤴️ ⚙️ 💖:

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

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

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

そして、依存関係とサブ依存関係だけで、それぞれに異なるパーミッション要件を追加することができます:

```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/"]

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"])

    public void filtered(FilteredCall<UserCQ, UserCQ> filteredLambda) {
        filtered(filteredLambda, null);
    }

    public void filtered(FilteredCall<UserCQ, UserCQ> filteredLambda, ConditionOptionCall<BoolQueryBuilder> opLambda) {
        bool((must, should, mustNot, filter) -> {
            filteredLambda.callback(must, filter);
        }, opLambda);
    }

    public void not(OperatorCall<UserCQ> notLambda) {

### `**user_in.dict()`について

#### Pydanticの`.dict()`

`user_in`は`UserIn`クラスのPydanticモデルです。

Pydanticモデルには、モデルのデータを含む`dict`を返す`.dict()`メソッドがあります。

そこで、以下のようなPydanticオブジェクト`user_in`を作成すると:

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

そして呼び出すと:

```Python
user_dict = user_in.dict()
```

# 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",

    }

    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",

# Declareer een variabele als een str
# en krijg editorondersteuning in de functie
def main(user_id: str):
    return user_id


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

Vervolgens kan je het op deze manier gebruiken:

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

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