An Artificial Neuron Network (ANN) with an arbitrary number of inputs and outputs

Question

I would like to use ANNs for my problem, but the issue is my inputs and outputs node numbers are not fixed.

I did some google search before asking my question and found that the RNN may help me with my problem. But, all examples which I've found are somehow have defined number of input and output nodes.

So, I'm looking for a strategy, how to make it real or at least some examples, preferable in Keras or PyTorch.

More details about my issue:

I have two inputs lists, where the length of the first one is fixed and equals two, f.e.:

in_1 = [2,2] 

but the length of the second list is flexible, the length can be from three to inf, f.e.:

in_2 = [1,1,2,2]

or

in_2 = [1,1,1,2,2,2,3,3,3]

Also, input lists depend on each other. The first list shows the dimension of the output list. So if in_1 = [2,2], means the output must have a possibility to be reshape to [2,2] form.

Currently, I'm thinking to combine two input list into one:

in = in_1 + in_2 = [2, 2, 1, 1, 2, 2]

Moreover, the output has the same length as the in_2 list, f.i.:

if input lists are:

in_1 = [2, 2]
in_2 = [1, 1, 2, 2]

Output should be:

out = [1, 2, 1, 2]

Any ideas are welcome!