The syntax `add = decorator(add)` after the function definition is not optimal. Python allows for a simpler way of using decorators:

```python
@decorator
def add(a, b):
    return a + b
```

It works as follows:

```python
def add(a, b)
    return a + b

add = decorator(add)
```



> Note
>
> The `@` symbol is syntactic sugar.  
**Syntactic sugar** is a programming language toolkit that makes writing code easier or better.

# Decorator Usage
Decorators can have any prescribed logic and be used for different situations. Here are some examples of useful decorators:

- **Timing Decorator:** A decorator that measures the time taken to execute a function.

- **Logging Decorator:** A decorator that logs the output of a function.

- **Validation Decorator:** A decorator that validates the input parameters of a function before execution.

Let's implement the validation decorator for this function:

```python
admin = {
    "username": "john123.bra",
    "password": "secret333password",
}

def auth(user: dict, username: str, password: str):
    if (
        username == user["username"] 
        and password == user["password"]
    ):
        print("User data is correct!")
        return True
    else:
        print("Wrong data!")
        return False
```

In the auth() function, the argument `user` is expected to be a dictionary with keys `"username"` and `"password"`, and other parameters are of type string.

The validation decorator should look like this:

admin = {
    "username": "john123.bra",
    "password": "secret333password",
}

def validate(func):
    def wrapper(user: dict, username: str, password: str) -> bool:
        validate_data = True
        
        if not isinstance(user, dict):
            validate_data = False
        
        if not user.get("username", False):
            validate_data = False
            
        if not user.get("password", False):
            validate_data = False
        
        if validate_data:
            return func(user, username, password)
        else:
            print("Wrong data!")
            return False
          
    return wrapper

@validate
def auth(user: dict, username: str, password: str) -> bool:
    if (
        username == user["username"] 
        and password == user["password"]
    ):
        print("User data is correct!")
        return True
    else:
        print("Wrong data!")
        return False
    
print(auth(admin, "1231", "12512"))
print(auth({1: 2}, "dsfs31", "safasf2"))
print(auth(admin, "john123.bra", "secret333password",))

In the example above, the `@validate` decorator wraps the `auth` function and prepares the `wrapper` actions for every call to the `auth` function. If the taken values are valid, the `if validate_data` code block should execute the enclosed `func` (`auth`) function and return the result. This way, we have no errors even if we pass an incorrect dictionary to the auth function.

The **username** and **password** do not require validation because the `==` comparison does not raise any errors. It always returns `False` if the data is unequal, regardless of the data types.

Discover the secrets of Python's Scopes, Closure, and Decorators in this immersive course. Gain mastery over variable visibility, unlock the power of closures for flexible function creation, and learn how decorators can enhance and modify your code dynamically. Elevate your Python skills and unleash the full potential of your programming abilities.

Python code distributes variables and objects across different environments isolated from each other. They are called scops. Let's study them, shall we?

To learn closure, you must complete the previous section, "Scopes".  The closure is used for decorators, caching, and other programming tools.

Decorators are widely used in real development. They open up many possibilities for us, and it is worth knowing how they work. To understand decorators, you need to know scopes and closures.

Mastering Python: Closures and Decorators

Decorator Usage

Decorator Usage