Ok, you can write a class of your own inheriting HingeLoss.
class loss_criteria(torch.nn.HingeEmbeddingLoss):
    def __init__(self,margin=1.0, size_average=True, reduce=True):
        super(loss_criteria, self).__init__(margin, size_average, reduce)
        
    def forward(self, cs, ct):
        D = 1.0 # constant value
        x = ((torch.abs(cs - ct))/(2*D))**2 #formula
        hinge_loss = torch.nn.HingeEmbeddingLoss()
        y = hinge_loss(x) # sorry, couldn't write the whole correct equation here
        return y

criterion = loss_criteria()
criterion(cs,ct)

This could help you! Try it out