code: def build_decoder(cls, args, task):
      text_decoder = cls.build_text_decoder(
          args, task.target_dictionary
      )
      compute_cross_attentive_loss = (
          True
          if getattr(args, "attentive_cost_regularization", 0.0) > 0.0
          else False
      )
      cross_attentive_loss_without_norm = getattr(
          args, "attentive_cost_without_normalize", False
      )
      cross_attentive_loss_reverse = (
          False  # getattr(args, "attentive_cost_reverse", False)
      )
      if getattr(args, "load_pretrained_text_decoder", "") != "":
          checkpoint_utils.load_pretrained_component_from_model(
              text_decoder, args.load_pretrained_text_decoder
          )
  
      if args.load_pretrained_speech_text_decoder:
          component_pairs = (("decoder.text_decoder", text_decoder),)
          cls.load_pretrained_speech_text_components(
              args.load_pretrained_speech_text_decoder, component_pairs
          )
  
      decoder = TransformerMultiInputDecoder(
          dictionary=task.target_dictionary,
          spch_decoder=text_decoder,
          text_decoder=text_decoder,
          compute_cross_attentive_loss=compute_cross_attentive_loss,
          cross_attentive_loss_with_norm=True
          if not cross_attentive_loss_without_norm
          else False,
          cross_attentive_loss_reverse=cross_attentive_loss_reverse,
      )
      if getattr(args, "load_init_decoder", "") != "":
          checkpoint_utils.load_pretrained_component_from_model(
              decoder, args.load_init_decoder
          )
      return decoder
  
imports: import logging
  from collections import OrderedDict, namedtuple
  import torch.nn
  from fairseq import checkpoint_utils, utils
  from fairseq.checkpoint_utils import load_checkpoint_to_cpu
  from fairseq.file_io import PathManager
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.speech_to_text import SpeechWavTransformerEncoder, StackedSpeechWavTransformerEncoder, TransformerDecoder
  from fairseq.models.transformer import TransformerEncoder
  from .s2t_dualinputtransformer import DualInputEncoder, DualInputS2TTransformerModel, TransformerMultiInputDecoder
label_desc: Use image node_id 9 for calling the DualInputWavTransformerModel obj's underlying member method code with example usage: obj.build_decoder(cls, args, task) and returns: decoder