MultiLabelSoftMarginLoss
class torch.nn.MultiLabelSoftMarginLoss(weight=None, size_average=None, reduce=None, reduction='mean')
Creates a criterion that optimizes a multi-label one-versus-all
   loss based on max-entropy, between input x and target y of size (N,
   C). For each sample in the minibatch:
  loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 +
  \exp(-x[i]))^{-1})                  + (1-y[i]) *
  \log\left(\frac{\exp(-x[i])}{(1 + \exp(-x[i]))}\right)

where i \in \left{0, \; \cdots , \; \text{x.nElement}() -
   1\right}, y[i] \in \left{0, \; 1\right}.
Parameters:
      * weight (Tensor, optional) -- a manual rescaling
        weight given to each class. If given, it has to be a Tensor of
        size C. Otherwise, it is treated as if having all ones.
  * **size_average** (*bool**, **optional*) -- Deprecated (see
    "reduction"). By default, the losses are averaged over each
