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 forward(self, acts, labels, act_lens, label_lens):
      assert len(labels.size()) == 2
      _assert_no_grad(labels)
      _assert_no_grad(act_lens)
      _assert_no_grad(label_lens)
      certify_inputs(acts, labels, act_lens, label_lens)
  
      # CPU Patch for fp16 - force cast to fp32
      if not acts.is_cuda and acts.dtype == torch.float16:
          acts = acts.float()
  
      if self.clamp > 0.0:
          acts = LogSoftmaxGradModification.apply(acts, self.clamp)
  
      acts = torch.nn.functional.log_softmax(acts, -1)
  
      return self.rnnt(
          acts,
          labels,
          act_lens,
          label_lens,
          self.blank,
          self.fastemit_lambda,
      )
  
imports: import numpy
  import torch
  from torch.autograd import Function, Variable
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the RNNTLoss obj's underlying member method code with example usage: obj.forward(acts, labels, act_lens, label_lens) and returns: self