code: def dualinputs2twavtransformer_large(args):
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 16
      )
      args.speech_encoder_layers = getattr(
          args, "speech_encoder_layers", 24
      )
      args.text_encoder_layers = getattr(
          args, "text_encoder_layers", 12
      )
      args.encoder_shared_text_layers_from_begin = getattr(
          args, "encoder_shared_text_layers_from_begin", 12
      )
      args.decoder_layers = getattr(args, "decoder_layers", 12)
      dualinputs2twavtransformer_base(args)
  
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 3 for calling a global function with example usage: dualinputs2twavtransformer_large(args) without return types