# und bekommen Sie Editor-Unterstützung innerhalb der Funktion
def main(user_id: str):
    return user_id


# Ein Pydantic-Modell
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Das kann nun wie folgt verwendet werden:

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

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

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",

# Değişkeni str olarak belirt
# ve o fonksiyon için harika bir editör desteği al
def main(user_id: str):
    return user_id


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

Sonrasında bu şekilde kullanabilirsin

```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/`

开发人员可以使用依赖项及其子依赖项为这些路径操作添加不同的权限：

```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/"]

Por ejemplo, digamos que tienes 4 endpoints de API (*path operations*):

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

entonces podrías agregar diferentes requisitos de permiso para cada uno de ellos solo con dependencias y sub-dependencias:

```mermaid
graph TB

    )
    return Token(access_token=access_token, token_type="bearer")


@app.get("/users/me/", response_model=User)
async def read_users_me(current_user: User = Depends(get_current_active_user)):
    return current_user


@app.get("/users/me/items/")
async def read_own_items(
    current_user: User = Security(get_current_active_user, scopes=["items"]),
):

# Déclare une variable comme étant une str
# et profitez de l'aide de votre IDE dans cette fonction
def main(user_id: str):
    return user_id


# Un modèle Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date
```
Qui peuvent ensuite être utilisés comme cela:

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

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

**Please note that when AD/LDAP is configured, MinIO will not support long term users defined internally.** Only AD/LDAP users (and the root user) are allowed. In addition to this, the server will not support operations on users or groups using `mc admin user` or `mc admin group` commands except `mc admin user info` and `mc admin group info` to list set policies for users and groups. This is because users and groups are defined externally in AD/LDAP.

## Configuring AD/LDAP on MinIO

# 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",