class_docstr: Numba class for multi-blank transducer loss (https://arxiv.org/pdf/2211.03541.pdf)
class_docstr_tok: ["Numba","class","for","multi-blank","transducer","loss","(","https",":","\/\/arxiv.org\/pdf\/2211.03541.pdf",")"]
docstr: big_blank_durations: list of durations for multi-blank transducer, e.g.
      [2, 4, 8].
  sigma: hyper-parameter for logit under-normalization method for training
      multi-blank transducers. Recommended value 0.05.
  Refer to https://arxiv.org/pdf/2211.03541 for detailed explanations for
      the above parameters;
  For other parameters for this class, refer to comment for class _RNNTNumba
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code: def forward(
      ctx,
      acts,
      labels,
      act_lens,
      label_lens,
      blank,
      big_blank_durations,
      reduction,
      fastemit_lambda,
      clamp,
      sigma,
  ):
      """
      big_blank_durations: list of durations for multi-blank transducer, e.g.
          [2, 4, 8].
      sigma: hyper-parameter for logit under-normalization method for training
          multi-blank transducers. Recommended value 0.05.
      Refer to https://arxiv.org/pdf/2211.03541 for detailed explanations for
          the above parameters;
      For other parameters for this class, refer to comment for class _RNNTNumba
      """
      is_cuda = acts.is_cuda
  
      certify_inputs(acts, labels, act_lens, label_lens)
      if clamp < 0:
          raise ValueError(
              "`clamp` must be 0.0 or positive float value."
          )
  
      if is_cuda:
          loss_func = rnnt.multiblank_rnnt_loss_gpu
      else:
          raise NotImplementedError()
  
      grads = torch.zeros_like(acts) if acts.requires_grad else None
      minibatch_size = acts.size(0)
      costs = torch.zeros(
          minibatch_size, device=acts.device, dtype=acts.dtype
      )
  
      loss_func(
          acts,
          labels=labels,
          input_lengths=act_lens,
          label_lengths=label_lens,
          costs=costs,
          grads=grads,
          blank_label=blank,
          big_blank_durations=big_blank_durations,
          fastemit_lambda=fastemit_lambda,
          clamp=clamp,
          sigma=sigma,
          num_threads=0,
      )
  
      if reduction in ["sum", "mean"]:
          costs = costs.sum().unsqueeze_(-1)
          if reduction == "mean":
              costs /= minibatch_size
  
              if grads is not None:
                  grads /= minibatch_size
  
      ctx.grads = grads
  
      return costs
  
imports: import torch
  from torch.autograd import Function
  from torch.nn import Module
  from nemo.collections.asr.parts.numba.rnnt_loss import rnnt
  from nemo.collections.asr.parts.numba.rnnt_loss.utils.cpu_utils import cpu_rnnt
label_desc: Use image node_id 1 for calling the _MultiblankRNNTNumba obj's underlying member method code with example usage: obj.forward(ctx, acts, labels, act_lens, label_lens, blank, big_blank_durations, reduction, fastemit_lambda, clamp, sigma) and returns: costs