code: def dualinputs2ttransformer_base(args):
      args.encoder_freezing_updates = getattr(
          args, "encoder_freezing_updates", 0
      )
      # Convolutional subsampler
      args.input_feat_per_channel = getattr(
          args, "input_feat_per_channel", 80
      )
      args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5")
      args.conv_channels = getattr(args, "conv_channels", 1024)
      # Transformer
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
      args.encoder_text_embed_dim = getattr(
          args, "encoder_text_embed_dim", args.encoder_embed_dim
      )
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 2048
      )
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 8
      )
      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", False
      )
  
      args.decoder_embed_dim = getattr(
          args, "decoder_embed_dim", args.encoder_embed_dim
      )
      args.decoder_ffn_embed_dim = getattr(
          args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
      )
      args.decoder_attention_heads = getattr(
          args, "decoder_attention_heads", 8
      )
      args.decoder_normalize_before = getattr(
          args, "decoder_normalize_before", True
      )
      args.decoder_learned_pos = getattr(
          args, "decoder_learned_pos", False
      )
      args.dropout = getattr(args, "dropout", 0.1)
      args.attention_dropout = getattr(
          args, "attention_dropout", args.dropout
      )
      args.activation_dropout = getattr(
          args, "activation_dropout", args.dropout
      )
      args.activation_fn = getattr(args, "activation_fn", "relu")
      args.adaptive_softmax_cutoff = getattr(
          args, "adaptive_softmax_cutoff", None
      )
      args.adaptive_softmax_dropout = getattr(
          args, "adaptive_softmax_dropout", 0
      )
      args.tie_adaptive_weights = getattr(
          args, "tie_adaptive_weights", False
      )
      args.share_decoder_input_output_embed = getattr(
          args, "share_decoder_input_output_embed", False
      )
      args.no_token_positional_embeddings = getattr(
          args, "no_token_positional_embeddings", False
      )
      args.adaptive_input = getattr(args, "adaptive_input", False)
      args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
      args.decoder_output_dim = getattr(
          args, "decoder_output_dim", args.decoder_embed_dim
      )
      args.layernorm_embedding = getattr(
          args, "layernorm_embedding", False
      )
      args.no_scale_embedding = getattr(
          args, "no_scale_embedding", False
      )
      args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
  
      args.speech_encoder_layers = getattr(
          args, "speech_encoder_layers", 10
      )
      args.text_encoder_layers = getattr(args, "text_encoder_layers", 6)
      args.encoder_shared_layers = getattr(
          args, "encoder_shared_layers", 0
      )
      args.decoder_layers = getattr(args, "decoder_layers", 6)
  
      args.add_speech_eos = getattr(args, "add_speech_eos", False)
  
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 1 for calling a global function with example usage: dualinputs2ttransformer_base(args) without return types