

Target: If containing class indices, shape (), (N) or (N, d_1,
        d_2, ..., d_K) with K \geq 1 in the case of K-dimensional loss
        where each value should be between [0, C). If containing class
        probabilities, same shape as the input and each value should
        be between [0, 1].
where:
 \begin{aligned}     C ={} & \text{number of classes} \\     N
 ={} & \text{batch size} \\ \end{aligned}



Examples:
  >>> # Example of target with class indices
  >>> input = torch.randn(3, 5, requires_grad=True)
  >>> target = torch.randint(5, (3,), dtype=torch.int64)
  >>> loss = F.cross_entropy(input, target)
  >>> loss.backward()
  >>>
  >>> # Example of target with class probabilities
  >>> input = torch.randn(3, 5, requires_grad=True)
  >>> target = torch.randn(3, 5).softmax(dim=1)
  >>> loss = F.cross_entropy(input, target)
  >>> loss.backward()
