This paper proposed a method for shot learning in heterogeneous attribute spaces, and shows that it performs well in a set of evaluations from synthetic tasks and OpenML datasets. Although 75% of the reviewers were leaning towards reject initially, the rebuttal and reviewer 3 convinced the dissenting majority (in fact, all reviewers who participated in the discussion) to lean towards supporting acceptance. The paper has a few outstanding weaknesses which can in part be addressed by revising the writing, and in part in future work, but for now it sufficiently proves the concept in a new area to warrant publication.