class_docstr: Parameters:
      blank (int, optional): blank label. Default: 0.
      reduction (string, optional): Specifies the reduction to apply to the output:
          'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
          'mean': the output losses will be divided by the target lengths and
          then the mean over the batch is taken. Default: 'mean'
      fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to
          FastEmit: Low-latency Streaming ASR with Sequence-level
          Emission Regularization.
      clamp: Float value. When set to value >= 0.0, will clamp the
          gradient to [-clamp, clamp].
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code: def __init__(
      self,
      blank=0,
      reduction="mean",
      fastemit_lambda: float = 0.0,
      clamp: float = -1,
  ):
      super(RNNTLossNumba, self).__init__()
      self.blank = blank
      self.fastemit_lambda = fastemit_lambda
      self.clamp = float(clamp) if clamp > 0 else 0.0
      self.reduction = reduction
      self.loss = _RNNTNumba.apply
  
imports: import torch
  from torch.autograd import Function
  from torch.nn import Module
  from espnet2.asr.transducer.rnnt_multi_blank import rnnt
  from espnet2.asr.transducer.rnnt_multi_blank.utils.cpu_utils import cpu_rnnt
label_desc: Use image node_id 1 to create a new RNNTLossNumba object from inherited base classes: Module with example: obj = RNNTLossNumba(blank, reduction, fastemit_lambda, clamp)