How to train model to predict 1 value from multiple input samples Very new in ML...

Actual problem is more complex, but I'll give it shorter.

Train data is a collection of samples, describing features of some items. There are many samples for each item. So ML model is implied to predict a few target variables, but one time for each item:



It appears, that I can't find in sklearn or a similar kit a way to fit model with this type of train-input/train-output. Is there any way to do this without hand-implementing whole pipeline? 

I've considered this answer: How to predict based on multiple samples?

But I'm not sure it's applicable, since there are many-many samples for each item

Edit 1:
Final model should get a few new samples for completely new item and predict same target variables
Is there really any benefit than just creating three different models, and combining them at the end? If the end use case is just to predict the best final models, then I would just train on them individually. 

You could have also do a NN with 3 output nodes, for each target. If you're just looking to do regression or something, I don't see why you wouldn't just predict them independently. There is some literature on multi-output regression (good summary here), but these involve scales of predicting high dimensional vectors(or even matrices), rather than just 3 numbers, so I don't know how useful this literature would be for your case, but that's where I would look if you're insistent on having a single model predict 3 outputs.