1. A computerized method of training a network of graph transformers comprising the steps of: generating a network of graph transformers, each graph transformer having an input receiving a first graph, having an output outputting a second graph, and having an internal function producing data in the second graph from data in the first graph; wherein said network of graph transformers includes: a) at least one tunable graph transformer having at least one tunable parameter being used to produce data in the second graph of the at least one tunable graph transformer from data in the first graph of the at least one tunable graph transformer; b) the internal function of each graph transformers differentiable with respect to data in the first graph and c) the internal function of the at least one tunable graph transformer also differentiable with respect to the at least one tunable parameter; and back-propagating gradients through the network to determine a minimum of a global objective function of the network; wherein said back-propagated gradients are used to train said network of graph transformers.