GaussianNLLLoss
class torch.nn.GaussianNLLLoss(*, full=False, eps=1e-06, reduction='mean')
Gaussian negative log likelihood loss.
The targets are treated as samples from Gaussian distributions with
   expectations and variances predicted by the neural network. For a
   "target" tensor modelled as having Gaussian distribution with a
   tensor of expectations "input" and a tensor of positive variances
   "var" the loss is:
  \text{loss} =
  \frac{1}{2}\left(\log\left(\text{max}\left(\text{var}, \
  \text{eps}\right)\right) + \frac{\left(\text{input} -
  \text{target}\right)^2} {\text{max}\left(\text{var}, \
  \text{eps}\right)}\right) + \text{const.}

where "eps" is used for stability. By default, the constant term of
   the loss function is omitted unless "full" is "True". If "var" is
   not the same size as "input" (due to a homoscedastic assumption),
   it must either have a final dimension of 1 or have one fewer