arbitrary shapes where we first locate tile placements and progressively fill the shape with the health of our network overall this work has three technical contributions first we model this tiling problem as an instance of graph learning second we design a graph convolutional neural network to predict tile placement to be a graph convolution here we call our network tile and GNN third we define loss terms directly on the network output so Thailand GNN can be trained with self supervision interactive design interface we provide