Can someone explain the Tensorflow code below?
gaussian is different from sigma, because sigma is trainable while gaussian is not. It implies that the value of sigma can be optimized but not of gaussian.
optimal is different from mse as

in the case of mse, the final loss is computed as MSE.
but in the case of optimal, we use gaussian as the loss function, with MSE acting as the variance of the gaussian kernel.