class_docstr: Criteron for the SpeechDLM model as described in the paper:
  https://arxiv.org/pdf/2203.16502.pdf
  
  There are 3 possible losses depending on the targets of the model:
      - general_unit_loss : The next unit prediction loss, corresponding to
          'next' target
      - edge_unit_loss : The edge unit prediction loss, corresponding to
          'edge' target
      - duration_loss : The duration prediction loss, corresponding to
          'duration' target
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docstr: Whether the logging outputs returned by `forward` can be summed
  across workers prior to calling `reduce_metrics`. Setting this
  to True will improves distributed training speed.
docstr_tok: ["Whether","the","logging","outputs","returned","by","`","forward","`","can","be","summed","across","workers","prior","to","calling","`","reduce_metrics","`",".","Setting","this","to","True","will","improves","distributed","training","speed","."]
code: def logging_outputs_can_be_summed() -> bool:
      """
      Whether the logging outputs returned by `forward` can be summed
      across workers prior to calling `reduce_metrics`. Setting this
      to True will improves distributed training speed.
      """
      return True
  
file_path: fairseq/fairseq/criterions/speech_dlm_criterion.py
imports: import math
  from dataclasses import dataclass, field
  from typing import Optional
  import torch.nn.functional
  from fairseq import metrics, utils
  from fairseq.criterions import FairseqCriterion, register_criterion
  from fairseq.dataclass import FairseqDataclass
  from omegaconf import II
label_desc: Use image node_id 5 for calling the SpeechDLMCriterion obj's underlying member method code with example usage: obj.logging_outputs_can_be_summed() and returns: True