code: def base_architecture(args):
      args.encoder_pretrained_embed = getattr(
          args, "encoder_pretrained_embed", None
      )
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 512
      )
      args.encoder_layers = getattr(args, "encoder_layers", 3)
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 4
      )
      args.encoder_freeze_embed = getattr(
          args, "encoder_freeze_embed", False
      )
      args.encoder_learned_pos = getattr(
          args, "encoder_learned_pos", False
      )
      args.encoder_normalize_before = getattr(
          args, "encoder_normalize_before", False
      )
      args.decoder_pretrained_embed = getattr(
          args, "decoder_pretrained_embed", 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", 3)
      args.decoder_attention_heads = getattr(
          args, "decoder_attention_heads", 4
      )
      args.decoder_out_embed_dim = getattr(
          args, "decoder_out_embed_dim", None
      )
      args.decoder_freeze_embed = getattr(
          args, "decoder_freeze_embed", False
      )
      args.decoder_learned_pos = getattr(
          args, "decoder_learned_pos", False
      )
      args.decoder_normalize_before = getattr(
          args, "decoder_normalize_before", False
      )
      args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0)
      args.decoder_layers_to_keep = getattr(
          args, "decoder_layers_to_keep", None
      )
      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.attention_dropout = getattr(args, "attention_dropout", 0.0)
      args.relu_dropout = getattr(args, "relu_dropout", 0.0)
      args.dropout = getattr(args, "dropout", 0.1)
      args.adaptive_softmax_cutoff = getattr(
          args, "adaptive_softmax_cutoff", None
      )
      args.aan = getattr(args, "aan", False)
      vocab_reduction.set_arg_defaults(args)
  
imports: import math
  import random
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import options, utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, transformer
  from fairseq.modules import AdaptiveSoftmax, LayerNorm, MultiheadAttention, SinusoidalPositionalEmbedding
  from pytorch_translate import utils, vocab_reduction
  from pytorch_translate.average_attention import AverageAttention
  from pytorch_translate.common_layers import TransformerEmbedding, TransformerEncoderGivenEmbeddings, TransformerTokenEmbedding, VariableTracker
  from pytorch_translate.semi_supervised import SemiSupervisedModel
  from pytorch_translate.utils import torch_find
label_desc: Use image node_id 2 for calling a global function with example usage: base_architecture(args) without return types