code: def levenshtein_base_architecture(args):
      args.encoder_embed_path = getattr(
          args, "encoder_embed_path", None
      )
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 2048
      )
      args.encoder_layers = getattr(args, "encoder_layers", 6)
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 8
      )
      args.encoder_normalize_before = getattr(
          args, "encoder_normalize_before", False
      )
      args.encoder_learned_pos = getattr(
          args, "encoder_learned_pos", False
      )
      args.decoder_embed_path = getattr(
          args, "decoder_embed_path", None
      )
      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_layers = getattr(args, "decoder_layers", 6)
      args.decoder_attention_heads = getattr(
          args, "decoder_attention_heads", 8
      )
      args.decoder_normalize_before = getattr(
          args, "decoder_normalize_before", False
      )
      args.decoder_learned_pos = getattr(
          args, "decoder_learned_pos", False
      )
      args.attention_dropout = getattr(args, "attention_dropout", 0.0)
      args.activation_dropout = getattr(args, "activation_dropout", 0.0)
      args.activation_fn = getattr(args, "activation_fn", "relu")
      args.dropout = getattr(args, "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", False
      )
      args.share_all_embeddings = getattr(
          args, "share_all_embeddings", False
      )
      args.no_token_positional_embeddings = getattr(
          args, "no_token_positional_embeddings", False
      )
      args.adaptive_input = getattr(args, "adaptive_input", False)
      args.apply_bert_init = getattr(args, "apply_bert_init", False)
  
      args.decoder_output_dim = getattr(
          args, "decoder_output_dim", args.decoder_embed_dim
      )
      args.sampling_for_deletion = getattr(
          args, "sampling_for_deletion", False
      )
      args.decoder_input_dim = getattr(
          args, "decoder_input_dim", args.decoder_embed_dim
      )
      args.early_exit = getattr(args, "early_exit", "6,6,6")
      args.no_share_discriminator = getattr(
          args, "no_share_discriminator", False
      )
      args.no_share_maskpredictor = getattr(
          args, "no_share_maskpredictor", False
      )
      args.share_discriminator_maskpredictor = getattr(
          args, "share_discriminator_maskpredictor", False
      )
      args.no_share_last_layer = getattr(
          args, "no_share_last_layer", False
      )
  
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 1 for calling a global function with example usage: levenshtein_base_architecture(args) without return types