def forward(self, mus, sigmas):
    loss = ((- 0.5) * torch.mean((((1 + (torch.log(sigmas) * 2.0)) - (mus * mus)) - (sigmas * sigmas))))
    return torch.max(loss, self.kl_tolerance.to(loss.device))
def forward(self, mus, sigmas):
    loss = ((- 0.5) * torch.mean((((1 + (torch.log(sigmas) * 2.0)) - (mus * mus)) - (sigmas * sigmas))))
    return torch.max(loss, self.kl_tolerance.to(loss.device))