code: def base_architecture(args):
      args.dropout = getattr(args, "dropout", 0.1)
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
      args.encoder_embed_path = getattr(
          args, "encoder_embed_path", None
      )
      args.encoder_layers = getattr(
          args, "encoder_layers", "[(512, 3)] * 20"
      )
      args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
      args.decoder_embed_path = getattr(
          args, "decoder_embed_path", None
      )
      args.decoder_layers = getattr(
          args, "decoder_layers", "[(512, 3)] * 20"
      )
      args.decoder_out_embed_dim = getattr(
          args, "decoder_out_embed_dim", 256
      )
      args.decoder_attention = getattr(
          args, "decoder_attention", "True"
      )
      args.share_input_output_embed = getattr(
          args, "share_input_output_embed", False
      )
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import AdaptiveSoftmax, BeamableMM, FairseqDropout, GradMultiply, LearnedPositionalEmbedding, LinearizedConvolution
label_desc: Use image node_id 7 for calling a global function with example usage: base_architecture(args) without return types