Yes, this makes sense to me, if you want to consider both the output of the pretrained model and y in a dense layer at the end of your model.
You might want to use net.eval() and net.train() on the pretrained model and set requires_grad depending on whether you want to train the pretrained model or keep the weights fixed while training your own model.