Hi, there seems to be an issue when some class is not present in the training set but in the test/validation set. For example, for MNIST classification, if I take out all digit ‘0’ from the training set, I got the error ‘RuntimeError: expected scalar type Long but found Float’ at step ‘train_loss += criterion(output, categorized_label[i].view(-1))’  under ‘model.eval()’, likely for a . I couldn’t see why in principle missing one class during training should pose such a problem. In my setting this missing of one class is necessary so for the time being I can only add very few data for that supposedly missing class as a compromise.