
zero_infinity (bool, optional) -- Whether to zero
        infinite losses and the associated gradients. Default: "False"
        Infinite losses mainly occur when the inputs are too short to
        be aligned to the targets.

Shape:
      * Log_probs: Tensor of size (T, N, C) or (T, C), where T =
        \text{input length}, N = \text{batch size}, and C =
        \text{number of classes (including blank)}. The logarithmized
        probabilities of the outputs (e.g. obtained with
        "torch.nn.functional.log_softmax()").
  * Targets: Tensor of size (N, S) or
    (\operatorname{sum}(\text{target\_lengths})), where N =
    \text{batch size} and S = \text{max target length, if shape is
    } (N, S). It represent the target sequences. Each element in
    the target sequence is a class index. And the target index
    cannot be blank (default=0). In the (N, S) form, targets are
    padded to the length of the longest sequence, and stacked. In
