MNIST is a particularly suitable data set for showing that rotation can occur after a convolutional encoder because its simplicity enables RFs to produce good predictions on the raw images and because multiple representations of the hand-drawn digits are available: images, 36 graphs (MNISTSuperpixel), and sequences (MNISTStroke). We employed three types of neural networks to embed the three MNIST representations. Specifically, we considered MNIST a binary classification task, assigning all digits lower than five to the positive class. For the embedding of the images, we employed the resnet18 12 architecture, whereas for MNISTSuperpixel we employed a GIN. The sequences of MNISTStroke we encoded with an LSTM, summing over all outputs. An MLP was connected to each convolutional encoder and trained end-to-end.