code: def forward(
      self,
      logits,
      logit_lengths,
      target_lengths,
      targets,
  ):
      log_probs = torch.nn.functional.log_softmax(logits, dim=-1)
      return _NumpyTransducer.apply(
          log_probs,
          logit_lengths,
          target_lengths,
          targets,
          self.blank,
      )
  
imports: import random
  import unittest
  import numpy
  import torch
  from torchaudio.functional import rnnt_loss
label_desc: Use image node_id 2 for calling the NumpyTransducerLoss obj's underlying member method code with example usage: obj.forward(logits, logit_lengths, target_lengths, targets) and returns: _NumpyTransducer