... the keyword starting the line that heads a block draws a lot of attention to it. This is true for "if", "while", "for", "try", "def" and "class". But the "using" keyword (or any other keyword in its place) doesn't deserve that attention; the emphasis should be on the decorator or decorators inside the suite, since those are the important modifiers to the function definition that follows. ...
In England, little is known of the trade and its structures before the late 13th century, at which paint guilds began to form, amongst them the Painters Company and the Stainers Company. These two guilds eventually merged with the consent of the Lord Mayor of the City of London in 1502, forming the Worshipful Company of Painter-Stainers. The guild standardised the craft and acted as a protector of the trade secrets. In 1599, the guild asked Parliament for protection, which was eventually granted in a bill of 1606, which granted the trade protection from outside competition such as plasterers.
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.
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:
You saw that, to define a decorator, you typically define a function returning a wrapper function. The wrapper function uses *args and **kwargs to pass on arguments to the decorated function. If you want your decorator to also take arguments, you need to nest the wrapper function inside another function. In this case, you usually end up with three return statements.
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 @.
Thanks to enhanced support for multi-core processors and CPUs that use AVX2 extensions and extensive code optimizations, this is the fastest version of Painter yet. A huge selection of brushes are noticeably faster — some as much as twice as fast. You can also take advantage of faster document rendering when zooming, panning and rotating — up to 50% faster.