code: def base_architecture(args):
      args.dropout = getattr(args, "dropout", 0.1)
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
      args.encoder_layers = getattr(
          args, "encoder_layers", "[(512, 3)] * 3"
      )
      args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
      args.decoder_layers = getattr(
          args, "decoder_layers", "[(512, 3)] * 8"
      )
      args.decoder_out_embed_dim = getattr(
          args, "decoder_out_embed_dim", 256
      )
      args.decoder_attention = getattr(
          args, "decoder_attention", "True"
      )
      args.self_attention = getattr(args, "self_attention", "False")
      args.encoder_attention = getattr(
          args, "encoder_attention", "False"
      )
      args.multihead_attention_nheads = getattr(
          args, "multihead_attention_nheads", 1
      )
      args.multihead_self_attention_nheads = getattr(
          args, "multihead_self_attention_nheads", 1
      )
      args.encoder_attention_nheads = getattr(
          args, "encoder_attention_nheads", 1
      )
      args.project_input = getattr(args, "project_input", "False")
      args.gated_attention = getattr(args, "gated_attention", "False")
      args.downsample = getattr(args, "downsample", "False")
      args.pretrained_checkpoint = getattr(
          args, "pretrained_checkpoint", ""
      )
      args.pretrained = getattr(args, "pretrained", "False")
  
imports: import logging
  import math
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import checkpoint_utils
  from fairseq.incremental_decoding_utils import with_incremental_state
  from fairseq.models import CompositeEncoder, FairseqDecoder, FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.modules import DownsampledMultiHeadAttention, FairseqDropout, GradMultiply, LayerNorm, LearnedPositionalEmbedding, LinearizedConvolution
label_desc: Use image node_id 6 for calling a global function with example usage: base_architecture(args) without return types