reduction} = \text{`sum'.}   \end{cases}
 Note that this case is equivalent to the combination of
 "LogSoftmax" and "NLLLoss".



Probabilities for each class; useful when labels beyond a single
     class per minibatch item are required, such as for blended
     labels, label smoothing, etc. The unreduced (i.e. with
     "reduction" set to "'none'") loss for this case can be described
     as:
\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad l_n = -
\sum_{c=1}^C w_c \log \frac{\exp(x_{n,c})}{\sum_{i=1}^C
\exp(x_{n,i})} y_{n,c}

where x is the input, y is the target, w is the weight, C is the
 number of classes, and N spans the minibatch dimension as well as
 d_1, ..., d_k for the K-dimensional case. If "reduction" is not
 "'none'" (default "'mean'"), then
\ell(x, y) = \begin{cases}     \frac{\sum_{n=1}^N l_n}{N}, &
\text{if reduction} = \text{`mean';}\\       \sum_{n=1}^N l_n,


