tensor that is the concatenation of individual targets, the
        target_lengths must add up to the total length of the tensor.
  * Output: scalar. If "reduction" is "'none'", then (N) if input
    is batched or () if input is unbatched, where N = \text{batch
    size}.

Examples:
  >>> # Target are to be padded
  >>> T = 50      # Input sequence length
  >>> C = 20      # Number of classes (including blank)
  >>> N = 16      # Batch size
  >>> S = 30      # Target sequence length of longest target in batch (padding length)
  >>> S_min = 10  # Minimum target length, for demonstration purposes
  >>>
  >>> # Initialize random batch of input vectors, for *size = (T,N,C)
  >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_()
  >>>
  >>> # Initialize random batch of targets (0 = blank, 1:C = classes)
  >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long)
  >>>
