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In Python, a generator can be thought of as an iterator that contains a frozen stack frame. Whenever next() is called on the iterator, Python resumes the frozen frame, which executes normally until the next yield statement is reached. The generator's frame is then frozen again, and the yielded value is returned to the caller.
Specifically, while both can yield multiple times, suspending their execution and allowing re-entry at multiple entry points, they differ in coroutines' ability to control where execution continues immediately after they yield, while generators cannot, instead transferring control back to the generator's caller. [9]
Coroutines are functions that can yield control to each other - a form of co-operative multitasking without threads. Coroutines can be implemented as a library if the programming language provides either continuations or generators - so the distinction between coroutines and generators in practice is a technical detail.
In routing, a Ping-Pong scheme is a simple algorithm for distributing data packets across two paths.If you had two paths A and B, then the algorithm would randomly start with one of the paths and then switch back and forth between the two.
A generator call can then be used in place of a list, or other structure whose elements will be iterated over. Whenever the for loop in the example requires the next item, the generator is called, and yields the next item. Generators don't have to be infinite like the prime-number example above.
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