class_docstr: Transducer loss
  Detail of this loss function can be found in: Sequence Transduction with Recurrent Neural 
  Networks
  
  Arguments:
      fuse_softmax_backward (bool, optional) whether to fuse the backward of transducer loss with
          softmax. (default: True)
      opt (int, optional): pick the optimization level in [0, 1]. opt=1 picks a more optimized 
          algorithm. In some cases, opt=1 might fall back to opt=0. (default: 1)
      packed_input (bool, optional): whether to pack the output in a compact form with don't-care 
      data being removed. (default: False)
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code: def __init__(
      self, fuse_softmax_backward=True, opt=1, packed_input=False
  ):
      super(TransducerLoss, self).__init__()
      self.fuse_softmax_backward = fuse_softmax_backward
      self.opt = opt
      self.packed_input = packed_input
      self.dummy_batch_offset = torch.empty(0)
  
file_path: apex/apex/contrib/transducer/transducer.py
imports: import torch
  import transducer_loss_cuda
  import transducer_joint_cuda
label_desc: Use image node_id 1 to create a new TransducerLoss object from inherited base classes: torch.nn with example: obj = TransducerLoss(fuse_softmax_backward, opt, packed_input)