code: def levenshtein_transformer_vaswani_wmt_en_de_big(args):
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 4096
      )
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 16
      )
      args.encoder_normalize_before = getattr(
          args, "encoder_normalize_before", False
      )
      args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
      args.decoder_ffn_embed_dim = getattr(
          args, "decoder_ffn_embed_dim", 4096
      )
      args.decoder_attention_heads = getattr(
          args, "decoder_attention_heads", 16
      )
      args.dropout = getattr(args, "dropout", 0.3)
      levenshtein_base_architecture(args)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from fairseq.iterative_refinement_generator import DecoderOut
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder
  from fairseq.models.transformer import Embedding
  from fairseq.modules import TransformerDecoderLayer
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from .levenshtein_utils import _apply_del_words, _apply_ins_masks, _apply_ins_words, _fill, _get_del_targets, _get_ins_targets, _skip, _skip_encoder_out
label_desc: Use image node_id 3 for calling a global function with example usage: levenshtein_transformer_vaswani_wmt_en_de_big(args) without return types