End to End trainable SVM in pytorch


I am using graphical neural networks to extract feature representation of the input data and then passing it to the binary classifier. Currently, I am using an FC layer with BCE Loss, but I want to replace it with SVM. So I would like to know is there any way to implement SVM that is an end to end trainable in PyTorch.