code: def base_architecture(args):
      args.dropout = getattr(args, "dropout", 0.1)
      args.attention_dropout = getattr(args, "attention_dropout", 0.1)
      args.act_dropout = getattr(args, "act_dropout", 0.0)
  
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 4096
      )
      args.encoder_layers = getattr(args, "encoder_layers", 6)
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 8
      )
  
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
      args.share_encoder_input_output_embed = getattr(
          args, "share_encoder_input_output_embed", False
      )
      args.encoder_learned_pos = getattr(
          args, "encoder_learned_pos", False
      )
      args.no_token_positional_embeddings = getattr(
          args, "no_token_positional_embeddings", False
      )
      args.num_segment = getattr(args, "num_segment", 2)
  
      args.sentence_class_num = getattr(args, "sentence_class_num", 2)
      args.sent_loss = getattr(args, "sent_loss", False)
  
      args.apply_bert_init = getattr(args, "apply_bert_init", False)
  
      args.activation_fn = getattr(args, "activation_fn", "relu")
      args.pooler_activation_fn = getattr(
          args, "pooler_activation_fn", "tanh"
      )
      args.encoder_normalize_before = getattr(
          args, "encoder_normalize_before", False
      )
  
imports: import logging
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture
  from fairseq.modules import LayerNorm, SinusoidalPositionalEmbedding, TransformerSentenceEncoder
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import safe_hasattr
label_desc: Use image node_id 1 for calling a global function with example usage: base_architecture(args) without return types