+ This function is a tool for exactly solving the MAP problem in a Potts
+ model. This type of model is useful when you have a problem which
+ can be modeled as a bunch of binary decisions on some variables,
+ but you have some kind of labeling consistency constraint. This
+ means that there is some penalty for giving certain pairs of variables
+ different labels. So in addition to trying to figure out how to best
+ label each variable on its own, you have to worry about making the
+ labels pairwise consistent in some sense. The find_max_factor_graph_potts()
+ routine can be used to find the most probable/highest scoring
+ labeling for this type of model.
+ The implementation of this routine is based on the min_cut object.
+
+
+