class_docstr: Parameters:
      blank (int): standard blank label.
      durations: list of durations for TDT model, e.g.
          [0, 1, 2, 3, 4].
      sigma: hyper-parameter for logit under-normalization method for training
          TDT. Recommended value 0.05.
      omega: hyper-parameter for RNN-T loss for loss combination.
      Refer to https://arxiv.org/abs/2304.06795 for detailed explanations for
          the above parameters;
  
      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,
      durations=None,
      reduction="mean",
      fastemit_lambda: float = 0.0,
      clamp: float = -1,
      sigma: float = 0.0,
      omega: float = 0.0,
  ):
      super(TDTLossNumba, self).__init__()
      self.blank = blank
      self.durations = durations if durations is not None else []
      self.fastemit_lambda = fastemit_lambda
      self.clamp = float(clamp) if clamp > 0 else 0.0
      self.reduction = reduction
      self.loss = _TDTNumba.apply
      self.sigma = sigma
      self.omega = omega
  
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 to create a new TDTLossNumba object from inherited base classes: Module with example: obj = TDTLossNumba(blank, durations, reduction, fastemit_lambda, clamp, sigma, omega)