class_docstr: Parameters:
      `blank_label` (int): default 0 - label index of blank token
      fastemit_lambda: Float scaling factor for FastEmit regularization.
class_docstr_tok: ["Parameters",":","`","blank_label","`","(","int",")",":","default","0","-","label","index","of","blank","token","fastemit_lambda",":","Float","scaling","factor","for","FastEmit","regularization","."]
code: def __init__(
      self,
      blank: int = 0,
      fastemit_lambda: float = 0.0,
      clamp: float = -1.0,
  ):
      super(RNNTLoss, self).__init__()
      self.blank = blank
      self.fastemit_lambda = fastemit_lambda
      self.clamp = float(clamp) if clamp > 0 else 0.0
      self.rnnt = _RNNT.apply
  
imports: import numpy
  import torch
  from torch.autograd import Function, Variable
  from torch.nn import Module
label_desc: Use image node_id 1 to create a new RNNTLoss object from inherited base classes: Module with example: obj = RNNTLoss(blank, fastemit_lambda, clamp)