# How to design a neural network with arbitrary input and output length?

I am trying to build a neural network that has an input of $$n$$ pairs of integer values (where $$n$$ is random) and a corresponding output of a binary array with length $$n$$.

The input will be a set of integer value coordinates $$[(x_{1}, y_{1}), (x_{2}, y_{2}), (x_{3}, y_{3}), \dots, (x_{50}, y_{50}), \dots]$$, where each instance can be of various lengths, like $$[(x_{1}, y_{1}), (x_{2}, y_{2}), (x_{3}, y_{3}), \dots, (x_{52}, y_{52})]$$ or $$[(x_{1}, y_{1}), (x_{2}, y_{2}), (x_{3}, y_{3}), \dots, (x_{101}, y_{101})]$$, etc.

The output is a set of binary arrays with each instance having the same length as the corresponding input.

May I know if anyone has any recommendations on what neural network would fit this use case?