class_docstr: This is a composite loss that, given a list of model outputs and a list of targets,
  computes an average of losses for each output-target pair
code: def build_underlying_criterion(args, task):
      saved_criterion = args.criterion
      args.criterion = args.underlying_criterion
      assert saved_criterion != args.underlying_criterion
      underlying_criterion = task.build_criterion(args)
      args.criterion = saved_criterion
      return underlying_criterion
  
file_path: fairseq/fairseq/criterions/composite_loss.py
imports: from fairseq import utils
  from fairseq.criterions import LegacyFairseqCriterion, register_criterion
  from torch import nn
label_desc: Use image node_id 3 for calling the CompositeLoss obj's underlying member method code with example usage: obj.build_underlying_criterion(args, task) and returns: underlying_criterion