This feature encoder used in this study consisted of two linear layers (projecting from # features → 1,050 →2,100), with ReLU activation and two dropout layers (p = 0.05). Adding layer normalization was tested but did not improve model performance. To the best of the author's knowledge at the time of writing, this is only the second time that such an encoding scheme has been used, and the first time learned feature embeddings have been used with Graphormer as well as the first time for multi-class prediction tasks.