The discussion continued on and off on python-dev from February 2002 through July 2004. Hundreds and hundreds of posts were made, with people proposing many possible syntax variations. Guido took a list of proposals to EuroPython 2004 [7], where a discussion took place. Subsequent to this, he decided that we'd have the Java-style [10] @decorator syntax, and this appeared for the first time in 2.4a2. Barry Warsaw named this the 'pie-decorator' syntax, in honor of the Pie-thon Parrot shootout which occurred around the same time as the decorator syntax, and because the @ looks a little like a pie. Guido outlined his case [8] on Python-dev, including this piece [9] on some of the (many) rejected forms.
Component Interface: The component interface is an abstraction describing the behaviors of the components that you will eventually use in your program. Any objects that will use these components will do so through the interface, meaning that they are principally concerned with the abstraction (not the actual object). This is what allows both objects and wrapped objects to be considered to be the same type.
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The discussion continued on and off on python-dev from February 2002 through July 2004. Hundreds and hundreds of posts were made, with people proposing many possible syntax variations. Guido took a list of proposals to EuroPython 2004 [7], where a discussion took place. Subsequent to this, he decided that we'd have the Java-style [10] @decorator syntax, and this appeared for the first time in 2.4a2. Barry Warsaw named this the 'pie-decorator' syntax, in honor of the Pie-thon Parrot shootout which occurred around the same time as the decorator syntax, and because the @ looks a little like a pie. Guido outlined his case [8] on Python-dev, including this piece [9] on some of the (many) rejected forms.
Just take a look at the code again. In the if/else clause we are returning greet and welcome, not greet() and welcome(). Why is that? It’s because when you put a pair of parentheses after it, the function gets executed; whereas if you don’t put parenthesis after it, then it can be passed around and can be assigned to other variables without executing it. Did you get it? Let me explain it in a little bit more detail. When we write a = hi(), hi() gets executed and because the name is yasoob by default, the function greet is returned. If we change the statement to a = hi(name = "ali") then the welcome function will be returned. We can also do print hi()() which outputs now you are in the greet() function.
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.
Concrete Decorator: Here we have two concrete decorators, ExcitedMessageDecorator and QuizzicalMessageDecorator, each of which descends from the abstract MessageDecorator class. However, these decorators do have special behavior in that they override the GetMessage and PrintMessage behaviors and enhance them by calling the base version from MessageDecorator (which in turn delegates to the relevant IMessage object) and then appending exclamations.
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Decorator Abstractions: Our decorator abstraction takes the form of the abstract MessageDecorator class, which also implements IMessage. The MessageDecorator class has a constructor that accepts an IMessage object as a parameter and then assigns it to a private variable. For its part, MessageDecorator doesn’t have any special behaviors and simply delegates GetMessage and PrintMessage calls to whichever IMessage object was injected into it.


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In this snippet we have a class hierarchy with a SimpleMessage at the top. The SimpleMessage class has a constructor that accepts a content string as well as two methods: GetMessage; and, PrintMessage. Down the hierarchy we have three subclasses: ExcitedMessage; QuizzicalMessage; and, ExcitedAndQuizzicalMessage. The only difference in the subclasses is that they override the SimpleMessage constructor to change the content string and append various exclamations. When we instantiate various message objects, using the same content string, and iterate over them, each has their own unique output.

Syntactic constraints. Python is a syntactically simple language with fairly strong constraints on what can and can't be done without "messing things up" (both visually and with regards to the language parser). There's no obvious way to structure this information so that people new to the concept will think, "Oh yeah, I know what you're doing." The best that seems possible is to keep new users from creating a wildly incorrect mental model of what the syntax means.
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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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The current method for transforming functions and methods (for instance, declaring them as a class or static method) is awkward and can lead to code that is difficult to understand. Ideally, these transformations should be made at the same point in the code where the declaration itself is made. This PEP introduces new syntax for transformations of a function or method declaration.
Some commonly used decorators that are even built-ins in Python are @classmethod, @staticmethod, and @property. The @classmethod and @staticmethod decorators are used to define methods inside a class namespace that are not connected to a particular instance of that class. The @property decorator is used to customize getters and setters for class attributes. Expand the box below for an example using these decorators.

Here we ensure that the key student_id is part of the request. Although this validation works, it really does not belong in the function itself. Plus, perhaps there are other routes that use the exact same validation. So, let’s keep it DRY and abstract out any unnecessary logic with a decorator. The following @validate_json decorator will do the job:
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