torch.nn.functional.cross_entropy
torch.nn.functional.cross_entropy(input, target, weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean', label_smoothing=0.0)
This criterion computes the cross entropy loss between input logits
   and target.
See "CrossEntropyLoss" for details.
Parameters:
      * input (Tensor) -- Predicted unnormalized logits; see
        Shape section below for supported shapes.
  * **target** (*Tensor*) -- Ground truth class indices or class
    probabilities; see Shape section below for supported shapes.

  * **weight** (*Tensor**, **optional*) -- a manual rescaling
    weight given to each class. If given, has to be a Tensor of
    size *C*

  * **size_average** (*bool**, **optional*) -- Deprecated (see
    "reduction"). By default, the losses are averaged over each
    loss element in the batch. Note that for some losses, there
