Labelling Unsegmented Sequence Data with Recurrent Neural
      Networks: https://www.cs.toronto.edu/~graves/icml_2006.pdf
Note:
 In order to use CuDNN, the following must be satisfied: "targets"
 must be in concatenated format, all "input_lengths" must be *T*.
 blank=0, "target_lengths" \leq 256, the integer arguments must be
 of dtype "torch.int32".The regular implementation uses the (more
 common in PyTorch) *torch.long* dtype.

Note:
 In some circumstances when using the CUDA backend with CuDNN,
 this operator may select a nondeterministic algorithm to increase
 performance. If this is undesirable, you can try to make the
 operation deterministic (potentially at a performance cost) by
 setting "torch.backends.cudnn.deterministic = True". Please see
 the notes on Reproducibility for background.
