A variety of deep functional maps have been proposed recently, from fully supervised to totally unsupervised, with a range of loss functions as well as different regularization terms. However, it is still not clear what are minimum and sufficient ingredients of a deep functional map pipeline and whether such ingredients unify or generalize all recent work on deep functional maps. With a slight abuse of notation, we show minimum and sufficient conditions for obtaining state of the art results with any loss function, supervised as well as unsupervised. Furthermore, we propose a novel framework designed for both full-to-full as well as partial to full shape matching that achieves state of the art results on all benchmark datasets outperforming even the fully supervised methods by a large margin.