There is general agreement that syntactic support is desirable to the current state of affairs. Guido mentioned syntactic support for decorators [2] in his DevDay keynote presentation at the 10th Python Conference [3], though he later said [5] it was only one of several extensions he proposed there "semi-jokingly". Michael Hudson raised the topic [4] on python-dev shortly after the conference, attributing the initial bracketed syntax to an earlier proposal on comp.lang.python by Gareth McCaughan [6].
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Did you get it? We just applied the previously learned principles. This is exactly what the decorators do in Python! They wrap a function and modify its behaviour in one way or the another. Now you might be wondering that we did not use the @ anywhere in our code? That is just a short way of making up a decorated function. Here is how we could have run the previous code sample using @.

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In a previous article, we discussed how to use the strategy pattern to dynamically change an object’s behavior at runtime. Classically, polymorphism in object-oriented design is static and achieved through inheritance; however, with the strategy pattern you can accomplish the same goal dynamically. Indeed, this is an excellent way to handle situations when you need an object to exhibit different behavior at different times. However, it’s worth noting that the strategy pattern requires mutation of the object you’re working with. By using the strategy pattern, you are necessarily changing the algorithm that an object uses for a given behavior. In some situations, it may be preferable not to mutate a given object. Or more likely, you won’t even have the option of mutating an object because it may come from a codebase over which you have no control (such as an external library). Such cases are relatively common; however, it’s still possible to enhance an immutable object’s behavior. One effective means to do so is with the decorator pattern.
This difference becomes most important when there are several independent ways of extending functionality. In some object-oriented programming languages, classes cannot be created at runtime, and it is typically not possible to predict, at design time, what combinations of extensions will be needed. This would mean that a new class would have to be made for every possible combination. By contrast, decorators are objects, created at runtime, and can be combined on a per-use basis. The I/O Streams implementations of both Java and the .NET Framework incorporate the decorator pattern.
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Did you get it? We just applied the previously learned principles. This is exactly what the decorators do in Python! They wrap a function and modify its behaviour in one way or the another. Now you might be wondering that we did not use the @ anywhere in our code? That is just a short way of making up a decorated function. Here is how we could have run the previous code sample using @.
Overall unfamiliarity with the concept. For people who have a passing acquaintance with algebra (or even basic arithmetic) or have used at least one other programming language, much of Python is intuitive. Very few people will have had any experience with the decorator concept before encountering it in Python. There's just no strong preexisting meme that captures the concept.
The .__init__() method must store a reference to the function and can do any other necessary initialization. The .__call__() method will be called instead of the decorated function. It does essentially the same thing as the wrapper() function in our earlier examples. Note that you need to use the functools.update_wrapper() function instead of @functools.wraps.
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