..., d_K) with K \geq 1 in the case of K-dimensional loss,
        depending on the shape of the input. Otherwise, scalar.
  where:

     \begin{aligned}     C ={} & \text{number of classes} \\     N
     ={} & \text{batch size} \\ \end{aligned}

Examples:
  >>> # Example of target with class indices
  >>> loss = nn.CrossEntropyLoss()
  >>> input = torch.randn(3, 5, requires_grad=True)
  >>> target = torch.empty(3, dtype=torch.long).random_(5)
  >>> output = loss(input, target)
  >>> output.backward()
  >>>
  >>> # Example of target with class probabilities
  >>> input = torch.randn(3, 5, requires_grad=True)
  >>> target = torch.randn(3, 5).softmax(dim=1)
  >>> output = loss(input, target)
  >>> output.backward()
