}

    @Test
    public void test_escapeValue_noSpecialChars() {
        assertEquals("admin", LdapUtil.escapeValue("admin"));
        assertEquals("john.doe", LdapUtil.escapeValue("john.doe"));
        assertEquals("user123", LdapUtil.escapeValue("user123"));
        assertEquals("Test User", LdapUtil.escapeValue("Test User"));
    }

    @Test
    public void test_escapeValue_backslash() {

Ainsi, si nous créons un objet Pydantic `user_in` comme :

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

et que nous appelons ensuite :

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

```

就能以如下方式调用：

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

现在，变量 `user_dict` 中的是包含数据的 `dict`（它是 `dict`，不是 Pydantic 模型对象）。

以如下方式调用：

```Python
print(user_dict)
```

输出的就是 Python `dict`：

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': 'john.doe@example.com',
    'full_name': None,
}
```

Pydantic models have a `.model_dump()` method that returns a `dict` with the model's data.

So, if we create a Pydantic object `user_in` like:

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

and then we call:

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

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

Pydantic modellerinde, model verilerini içeren bir `dict` döndüren `.model_dump()` metodu bulunur.

Yani, şöyle bir Pydantic nesnesi `user_in` oluşturursak:

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

ve sonra şunu çağırırsak:

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

Поэтому, если мы создадим Pydantic-объект `user_in` таким способом:

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

и затем вызовем:

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

то теперь у нас есть `dict` с данными в переменной `user_dict` (это `dict` вместо объекта Pydantic-модели).

Então, se criarmos um objeto Pydantic `user_in` como:

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

e depois chamarmos:

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

agora temos um `dict` com os dados na variável `user_dict` (é um `dict` em vez de um objeto de modelo Pydantic).

Así que, si creamos un objeto Pydantic `user_in` como:

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

y luego llamamos a:

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

ahora tenemos un `dict` con los datos en la variable `user_dict` (es un `dict` en lugar de un objeto modelo Pydantic).

Wenn wir also ein Pydantic-Objekt `user_in` erstellen, etwa so:

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

und dann aufrufen:

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

user_in = UserIn(username="john", password="secret", email="john.doe@example.com")
```

接著呼叫：

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

此時變數 `user_dict` 會是一個承載資料的 `dict`（也就是 `dict`，而非 Pydantic 模型物件）。

若再呼叫：

```Python
print(user_dict)
```

我們會得到一個 Python `dict`：

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': 'john.doe@example.com',
    'full_name': None,
}
```