class_docstr: Transducer tasks module.
docstr: Forward LM loss.
  
  Args:
      dec_out: Decoder output sequences. (B, U, D_dec)
      target: Target label ID sequences. (B, U)
  
  Returns:
      : LM loss value.
docstr_tok: ["Forward","LM","loss",".","Args",":","dec_out",":","Decoder","output","sequences",".","(","B",",","U",",","D_dec",")","target",":","Target","label","ID","sequences",".","(","B",",","U",")","Returns",":",":","LM","loss","value","."]
code: def compute_lm_loss(
      self,
      dec_out: torch.Tensor,
      target: torch.Tensor,
  ) -> torch.Tensor:
      """Forward LM loss.
  
      Args:
          dec_out: Decoder output sequences. (B, U, D_dec)
          target: Target label ID sequences. (B, U)
  
      Returns:
          : LM loss value.
  
      """
      lm_lin = self.lm_lin(dec_out)
  
      lm_loss = self.label_smoothing_loss(lm_lin, target)
  
      return lm_loss
  
file_path: espnet/espnet/nets/pytorch_backend/transducer/transducer_tasks.py
imports: from typing import Any, List, Optional, Tuple
  import torch
  from espnet.nets.pytorch_backend.nets_utils import pad_list
  from espnet.nets.pytorch_backend.transducer.joint_network import JointNetwork
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 5 for calling the TransducerTasks obj's underlying member method code with example usage: obj.compute_lm_loss(dec_out, target) and returns: lm_loss