code: def forward(self, nnet_output, ys_pad, hlens, ylens):
      # Reorder and filter out invalid examples:
      # A. K2 requires that hlens are in descending order;
      # B. remove all examples whose hlens is smaller than necessary.
      indices = torch.argsort(hlens, descending=True)
      ys, min_hlens = self.find_minimum_hlens(
          ys_pad[indices], ylens[indices]
      )
      valid_sample_indices = (min_hlens <= hlens[indices]).nonzero(
          as_tuple=True
      )[0]
  
      if len(valid_sample_indices) < 1:
          logging.warning(
              "All examples are invalid for Bayes Risk CTC. Skip this batch"
          )
          return torch.Tensor([0.0]).to(nnet_output.device)
  
      indices = indices[valid_sample_indices]
      nnet_output, hlens, ylens = (
          nnet_output[indices],
          hlens[indices],
          ylens[indices],
      )
      ys = [ys[i.item()] for i in valid_sample_indices]
  
      # Core implementation
      loss_utt = self.forward_core(nnet_output, ys, hlens, ylens)
  
      # Recover the original order. Invalid examples are excluded.
      indices2 = torch.argsort(indices)
      loss_utt = loss_utt[indices2]
  
      return loss_utt
  
file_path: espnet/espnet2/asr/bayes_risk_ctc.py
imports: import logging
  import _k2
  import k2
  import torch
label_desc: Use image node_id 2 for calling the BayesRiskCTC obj's underlying member method code with example usage: obj.forward(nnet_output, ys_pad, hlens, ylens) and returns: loss_utt, torch