code: def build_decoder(cls, args, task):
      dec_cfg = {
          "decoder_layerdrop": args.decoder_layerdrop,
          "share_decoder_input_output_embed": args.share_decoder_input_output_embed,
          "decoder_embed_dim": args.decoder_embed_dim,
          "max_target_positions": args.max_target_positions,
          "dropout": args.dropout,
          "encoder_learned_pos": args.encoder_learned_pos,
          "decoder_learned_pos": args.decoder_learned_pos,
          "layernorm_embedding": args.layernorm_embedding,
          "decoder_normalize_before": args.decoder_normalize_before,
          "activation_dropout": args.activation_dropout,
          "attention_dropout": args.attention_dropout,
          "decoder_ffn_embed_dim": args.decoder_ffn_embed_dim,
          "decoder_layers": args.decoder_layers,
          "decoder_attention_heads": args.decoder_attention_heads,
          "decoder_output_dim": args.decoder_embed_dim,
          "no_scale_embedding": args.no_scale_embedding,
          "adaptive_input": args.adaptive_input,
          "quant_noise_pq": args.quant_noise_pq,
          "adaptive_softmax_cutoff": args.adaptive_softmax_cutoff,
          "tie_adaptive_weights": args.tie_adaptive_weights,
          "no_token_positional_embeddings": args.no_token_positional_embeddings,
          "encoder": {"embed_dim": args.encoder_embed_dim},
      }
      dec_cfg = namedtuple("args", dec_cfg.keys())(*dec_cfg.values())
      dec_emb = nn.Embedding(
          len(task.target_dictionary),
          args.decoder_embed_dim,
          task.target_dictionary.pad(),
      )
      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)
      )
  
      text_decoder = TransformerDecoder(
          dec_cfg, task.target_dictionary, dec_emb
      )
      spch_decoder = TransformerDecoder(
          dec_cfg, task.target_dictionary, dec_emb
      )
      spch_decoder = TransformerMultiInputDecoder.share_spchdecoder(
          args, text_decoder, spch_decoder
      )
      decoder = TransformerMultiInputDecoder(
          dictionary=task.target_dictionary,
          spch_decoder=spch_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 args.init_scale != 1.0:
          with torch.no_grad():
              for param in decoder.parameters():
                  param.data.mul_(args.init_scale)
      if args.load_pretrain_decoder != "":
          try:
              checkpoint_utils.load_pretrained_component_from_model(
                  decoder, args.load_pretrain_decoder
              )
          except RuntimeError:
              checkpoint_utils.load_pretrained_component_from_model(
                  decoder.text_decoder, args.load_pretrain_decoder
              )
              if args.decoder_shared_layer_level > 0:
                  checkpoint_utils.load_pretrained_component_from_model(
                      decoder.spch_decoder, args.load_pretrain_decoder
                  )
  
      return decoder
  
imports: import logging
  from collections import namedtuple
  import torch
  import torch.nn
  from fairseq import checkpoint_utils
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqDecoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.fairseq_encoder import EncoderOut
  from fairseq.models.speech_to_text import TransformerDecoder, S2TTransformerEncoder
  from fairseq.models.transformer import TransformerEncoder
  from fairseq.modules import TransformerEncoderLayer, GradMultiply, LayerNorm
label_desc: Use image node_id 5 for calling the DualInputS2TTransformerModel obj's underlying member method code with example usage: obj.build_decoder(cls, args, task) and returns: decoder