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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:
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.

In general, functions in Python may also have side effects rather than just turning an input into an output. The print() function is a basic example of this: it returns None while having the side effect of outputting something to the console. However, to understand decorators, it is enough to think about functions as something that turns given arguments into a value.
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In 1894, a national association formed, recreating itself in 1918 as the National Federation of Master Painters and Decorators of England and Wales, then changing its name once again to the British Decorators Association before merging, in 2002, with the Painting & Decorating Federation to form the Painting & Decorating Association. The Construction Industry Joint Council, a body formed of both unions and business organizations, today has responsibility for the setting of pay levels.[2]
A veduta is a highly detailed, usually large-scale painting of a cityscape or some other vista. This genre of landscape originated in Flanders, where artists such as Paul Bril painted vedute as early as the 16th century. As the itinerary of the Grand Tour became somewhat standardized, vedute of familiar scenes like the Roman Forum or the Grand Canal recalled early ventures to the Continent for aristocratic Englishmen. In the later 19th century, more personal impressions of cityscapes replaced the desire for topographical accuracy, which was satisfied instead by painted panoramas.
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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].
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 @.
The primary objection to this form is that it requires "peeking inside" the method body to determine the decorators. In addition, even though the code is inside the method body, it is not executed when the method is run. Guido felt that docstrings were not a good counter-example, and that it was quite possible that a 'docstring' decorator could help move the docstring to outside the function body.
The decorator pattern can be used to extend (decorate) the functionality of a certain object statically, or in some cases at run-time, independently of other instances of the same class, provided some groundwork is done at design time. This is achieved by designing a new Decorator class that wraps the original class. This wrapping could be achieved by the following sequence of steps:
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.
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This decorator works by storing the time just before the function starts running (at the line marked # 1) and just after the function finishes (at # 2). The time the function takes is then the difference between the two (at # 3). We use the time.perf_counter() function, which does a good job of measuring time intervals. Here are some examples of timings:
Acrylic paint is fast drying paint containing pigment suspension in acrylic polymer emulsion. Acrylic paints can be diluted with water, but become water-resistant when dry. Depending on how much the paint is diluted (with water) or modified with acrylic gels, media, or pastes, the finished acrylic painting can resemble a watercolor or an oil painting, or have its own unique characteristics not attainable with other media. The main practical difference between most acrylics and oil paints is the inherent drying time. Oils allow for more time to blend colors and apply even glazes over under-paintings. This slow drying aspect of oil can be seen as an advantage for certain techniques, but may also impede the artist's ability to work quickly.
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