code: def dualinputxmtransformer_base(args):
      # wav2vec encoder
      set_default_w2v_encoder_args(args)
      set_default_adaptor_args(args)
  
      # mbart model
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim
      )
      args.encoder_layers = getattr(args, "encoder_layers", 12)
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 16
      )
      args.encoder_normalize_before = getattr(
          args, "encoder_normalize_before", True
      )
      args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0)
      args.encoder_learned_pos = getattr(
          args, "encoder_learned_pos", True
      )
  
      args.decoder_embed_path = getattr(
          args, "decoder_embed_path", None
      )
      args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
      args.decoder_ffn_embed_dim = getattr(
          args, "decoder_ffn_embed_dim", 4 * 1024
      )
      args.decoder_layers = getattr(args, "decoder_layers", 12)
      args.decoder_attention_heads = getattr(
          args, "decoder_attention_heads", 16
      )
      args.decoder_normalize_before = getattr(
          args, "decoder_normalize_before", True
      )
      args.decoder_learned_pos = getattr(
          args, "decoder_learned_pos", True
      )
      args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
  
      args.adaptive_input = getattr(args, "adaptive_input", False)
  
      args.mbart_attention_dropout = getattr(
          args, "mbart_attention_dropout", 0.0
      )
      args.mbart_activation_dropout = getattr(
          args, "mbart_activation_dropout", 0.0
      )
      args.mbart_dropout = getattr(args, "mbart_dropout", 0.1)
      args.adaptive_softmax_cutoff = getattr(
          args, "adaptive_softmax_cutoff", None
      )
      args.adaptive_softmax_dropout = getattr(
          args, "adaptive_softmax_dropout", 0
      )
      args.share_decoder_input_output_embed = getattr(
          args, "share_decoder_input_output_embed", True
      )
      args.no_token_positional_embeddings = getattr(
          args, "no_token_positional_embeddings", False
      )
  
      args.decoder_output_dim = getattr(
          args, "decoder_output_dim", args.decoder_embed_dim
      )
      args.decoder_input_dim = getattr(
          args, "decoder_input_dim", args.decoder_embed_dim
      )
  
      args.no_scale_embedding = getattr(
          args, "no_scale_embedding", False
      )
      args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
      args.layernorm_embedding = getattr(
          args, "layernorm_embedding", True
      )
  
      args.activation_fn = getattr(args, "activation_fn", "gelu")
      args.pooler_activation_fn = getattr(
          args, "pooler_activation_fn", "tanh"
      )
      args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
  
imports: import copy
  import torch.nn
  from fairseq import checkpoint_utils
  from fairseq import utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import register_model, register_model_architecture, FairseqEncoder
  from fairseq.models.speech_to_text import Wav2VecEncoderWithAdaptor
  from fairseq.models.speech_to_text.xm_transformer import set_default_adaptor_args, set_default_w2v_encoder_args, need_finetuning
  from fairseq.models.transformer import TransformerEncoder, TransformerDecoder
  from fairseq.models.wav2vec import TransformerSentenceEncoderLayer
  from fairseq.utils import safe_hasattr
  from .s2t_dualinputtransformer import DualInputS2TTransformerModel, TransformerMultiInputDecoder, DualInputEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: dualinputxmtransformer_base(args) without return types