code: def add_args(parser):
      def add_transformer_args(parser):
          # We can't use TransformerModel.add_args(parser), since it defines max-source-positions which is duplicated with tasks/speech_to_text.py
          # Transformer
          parser.add_argument(
              "--activation-fn",
              type=str,
              default="relu",
              choices=utils.get_available_activation_fns(),
              help="activation function to use",
          )
          parser.add_argument(
              "--dropout",
              type=float,
              metavar="D",
              help="dropout probability",
          )
          parser.add_argument(
              "--attention-dropout",
              type=float,
              metavar="D",
              help="dropout probability for attention weights",
          )
          parser.add_argument(
              "--activation-dropout",
              "--relu-dropout",
              type=float,
              metavar="D",
              help="dropout probability after activation in FFN.",
          )
          parser.add_argument(
              "--encoder-embed-dim",
              type=int,
              metavar="N",
              help="encoder embedding dimension",
          )
          parser.add_argument(
              "--encoder-ffn-embed-dim",
              type=int,
              metavar="N",
              help="encoder embedding dimension for FFN",
          )
          parser.add_argument(
              "--encoder-layers",
              type=int,
              metavar="N",
              help="num encoder layers",
          )
          parser.add_argument(
              "--encoder-attention-heads",
              type=int,
              metavar="N",
              help="num encoder attention heads",
          )
          parser.add_argument(
              "--encoder-normalize-before",
              action="store_true",
              help="apply layernorm before each encoder block",
          )
          parser.add_argument(
              "--decoder-embed-dim",
              type=int,
              metavar="N",
              help="decoder embedding dimension",
          )
          parser.add_argument(
              "--decoder-ffn-embed-dim",
              type=int,
              metavar="N",
              help="decoder embedding dimension for FFN",
          )
          parser.add_argument(
              "--decoder-layers",
              type=int,
              metavar="N",
              help="num decoder layers",
          )
          parser.add_argument(
              "--decoder-attention-heads",
              type=int,
              metavar="N",
              help="num decoder attention heads",
          )
          parser.add_argument(
              "--decoder-normalize-before",
              action="store_true",
              help="apply layernorm before each decoder block",
          )
          parser.add_argument(
              "--share-decoder-input-output-embed",
              action="store_true",
              help="share decoder input and output embeddings",
          )
          parser.add_argument(
              "--layernorm-embedding",
              action="store_true",
              help="add layernorm to embedding",
          )
          parser.add_argument(
              "--no-scale-embedding",
              action="store_true",
              help="if True, dont scale embeddings",
          )
  
          parser.add_argument(
              "--encoder-learned-pos",
              action="store_true",
              help="use learned positional embeddings",
          )
          parser.add_argument(
              "--decoder-learned-pos",
              action="store_true",
              help="use learned positional embeddings",
          )
  
      add_transformer_args(parser)
      SpeechWavTransformerEncoder.add_args(parser)
      parser.add_argument(
          "--load-pretrained-speech-text-encoder",
          type=str,
          default="",
          metavar="EXPR",
          help=""" path to the pretrained speech text encoder from SpeechTextPreTrainModel """,
      )
      parser.add_argument(
          "--load-pretrained-wav2vec-encoder",
          type=str,
          default="",
          metavar="EXPR",
          help=""" path to the pretrained speech text encoder from wav2vec """,
      )
  
      parser.add_argument(
          "--load-pretrained-speech-text-decoder",
          type=str,
          default="",
          metavar="EXPR",
          help=""" path to the pretrained speech text decoder from SpeechTextPreTrainModel """,
      )
      parser.add_argument(
          "--load-pretrained-text-decoder",
          type=str,
          default="",
          metavar="EXPR",
          help=""" path to the pretrained  text decoder """,
      )
      parser.add_argument(
          "--load-init-encoder",
          type=str,
          default="",
          metavar="EXPR",
          help=""" path to load seed encoder model """,
      )
      parser.add_argument(
          "--load-init-decoder",
          type=str,
          default="",
          metavar="EXPR",
          help=""" path to load seed decoder model """,
      )
  
      parser.add_argument(
          "--text-input-cost-ratio",
          type=float,
          default=1.0,
          metavar="V",
          help="text input cost ratio relative to speech input cost",
      )
      parser.add_argument(
          "--enc-grad-mult",
          type=float,
          metavar="V",
          default=1.0,
          help="multiply enc1 and enc2 gradient by V",
      )
      parser.add_argument(
          "--enc2-along-grad-mult",
          type=float,
          metavar="V",
          default=1.0,
          help="multiply enc2 gradient by V if only enc2 is used",
      )
      parser.add_argument(
          "--no-strict-check-pretrain-model",
          action="store_true",
          help="Don't apply strict model check for the pretrained model",
      )
  
      parser.add_argument(
          "--stacked-encoder",
          action="store_true",
          help="stack speech and text encoders",
      )
  
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 2 for calling the DualInputWavTransformerModel obj's underlying member method code with example usage: obj.add_args(parser) without return types