class_docstr: Class for managing efficient loss computation. Handles
  accumulating multiple loss computations.
  
  Args:
      criterion (:obj:`nn. loss function`) : NLLoss or customed loss
      generator (:obj:`nn.Module`) :
      copy_attn (bool): whether copy attention mechanism is on/off
      lambda_coverage: Hyper-param to apply coverage attention if any
      lambda_align: Hyper-param for alignment loss
      tgt_shift_index (int): 1 for NMT, 0 for LM
      vocab: target vocab (for copy attention score calculation)
           module that maps the output of the decoder to a
           distribution over the target vocabulary.
      lm_generator (:obj:`ctranslate2.Generator`): LM Generator
      lm_prior_lambda (float): weight of LM model in loss
      lm_prior_tau (float): scaler for LM loss
class_docstr_tok: ["Class","for","managing","efficient","loss","computation",".","Handles","accumulating","multiple","loss","computations",".","Args",":","criterion","(",":","obj",":","`","nn",".","loss","function","`",")",":","NLLoss","or","customed","loss","generator","(",":","obj",":","`","nn.Module","`",")",":","copy_attn","(","bool",")",":","whether","copy","attention","mechanism","is","on\/off","lambda_coverage",":","Hyper-param","to","apply","coverage","attention","if","any","lambda_align",":","Hyper-param","for","alignment","loss","tgt_shift_index","(","int",")",":","1","for","NMT",",","0","for","LM","vocab",":","target","vocab","(","for","copy","attention","score","calculation",")","module","that","maps","the","output","of","the","decoder","to","a","distribution","over","the","target","vocabulary",".","lm_generator","(",":","obj",":","`","ctranslate2.Generator","`",")",":","LM","Generator","lm_prior_lambda","(","float",")",":","weight","of","LM","model","in","loss","lm_prior_tau","(","float",")",":","scaler","for","LM","loss"]
docstr: compute coverage loss
docstr_tok: ["compute","coverage","loss"]
code: def _compute_coverage_loss(self, std_attn, cov_attn, tgt):
      """compute coverage loss"""
      zero_attn = torch.zeros(
          cov_attn.size()[1:], device=cov_attn.device
      )
      cov_attn = torch.cat((zero_attn.unsqueeze(0), cov_attn[:-1]), 0)
      covloss = torch.min(std_attn, cov_attn).sum(dim=-1).view(-1)
  
      covloss[tgt == self.padding_idx] = 0
      return covloss.sum()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import onmt
  from onmt.modules.sparse_losses import SparsemaxLoss
  from onmt.modules.sparse_activations import LogSparsemax
  from onmt.constants import ModelTask, DefaultTokens
  from onmt.modules.copy_generator import collapse_copy_scores
  from onmt.model_builder import load_test_model
label_desc: Use image node_id 4 for calling the LossCompute obj's underlying member method code with example usage: obj._compute_coverage_loss(std_attn, cov_attn, tgt) and returns: covloss