parameters: (acts, labels, act_lens, label_lens, blank=0, reduction='mean')
return_statement: return _RNNT.apply(acts, labels, act_lens, label_lens, blank, reduction)
docstring: RNN Transducer Loss
  
      Args:
          acts: Tensor of (batch x seqLength x labelLength x outputDim) containing output from network
          labels: 2 dimensional Tensor containing all the targets of the batch with zero padded
          act_lens: Tensor of size (batch) containing size of each output sequence from the network
          label_lens: Tensor of (batch) containing label length of each example
          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'
docstring_summary: RNN Transducer Loss
docstring_tokens: RNN Transducer Loss
function: def rnnt_loss(acts, labels, act_lens, label_lens, blank=0, reduction='mean'):
      """ RNN Transducer Loss
  
      Args:
          acts: Tensor of (batch x seqLength x labelLength x outputDim) containing output from network
          labels: 2 dimensional Tensor containing all the targets of the batch with zero padded
          act_lens: Tensor of size (batch) containing size of each output sequence from the network
          label_lens: Tensor of (batch) containing label length of each example
          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'
      """
      if not acts.is_cuda:
          acts = torch.nn.functional.log_softmax(acts, -1)
  
      return _RNNT.apply(acts, labels, act_lens, label_lens, blank, reduction)
function_tokens: def rnnt_loss ( acts , labels , act_lens , label_lens , blank = 0 , reduction = 'mean' ) : if not acts . is_cuda : acts = torch . nn . functional . log_softmax ( acts , - 1 ) return _RNNT . apply ( acts , labels , act_lens , label_lens , blank , reduction )