code: def base_architecture(args):
      args.encoder_layers = safe_getattr(args, "encoder_layers", 12)
      args.encoder_embed_dim = safe_getattr(
          args, "encoder_embed_dim", 768
      )
      args.encoder_ffn_embed_dim = safe_getattr(
          args, "encoder_ffn_embed_dim", 3072
      )
      args.encoder_attention_heads = safe_getattr(
          args, "encoder_attention_heads", 12
      )
  
      args.dropout = safe_getattr(args, "dropout", 0.1)
      args.attention_dropout = safe_getattr(
          args, "attention_dropout", 0.1
      )
      args.activation_dropout = safe_getattr(
          args, "activation_dropout", 0.0
      )
      args.pooler_dropout = safe_getattr(args, "pooler_dropout", 0.0)
  
      args.max_source_positions = safe_getattr(
          args, "max_positions", 512
      )
      args.no_token_positional_embeddings = safe_getattr(
          args, "no_token_positional_embeddings", False
      )
  
      # BERT has a few structural differences compared to the original Transformer
      args.encoder_learned_pos = safe_getattr(
          args, "encoder_learned_pos", True
      )
      args.layernorm_embedding = safe_getattr(
          args, "layernorm_embedding", True
      )
      args.no_scale_embedding = safe_getattr(
          args, "no_scale_embedding", True
      )
      args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
      args.encoder_normalize_before = safe_getattr(
          args, "encoder_normalize_before", False
      )
      args.pooler_activation_fn = safe_getattr(
          args, "pooler_activation_fn", "tanh"
      )
      args.untie_weights_roberta = safe_getattr(
          args, "untie_weights_roberta", False
      )
  
      # Adaptive input config
      args.adaptive_input = safe_getattr(args, "adaptive_input", False)
  
      # LayerDrop config
      args.encoder_layerdrop = safe_getattr(
          args, "encoder_layerdrop", 0.0
      )
      args.encoder_layers_to_keep = safe_getattr(
          args, "encoder_layers_to_keep", None
      )
  
      # Quantization noise config
      args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0)
      args.quant_noise_pq_block_size = safe_getattr(
          args, "quant_noise_pq_block_size", 8
      )
      args.quant_noise_scalar = safe_getattr(
          args, "quant_noise_scalar", 0
      )
  
      # R4F config
      args.spectral_norm_classification_head = safe_getattr(
          args, "spectral_norm_classification_head", 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.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncoder
  from fairseq.modules import LayerNorm
  from fairseq.modules.quant_noise import quant_noise
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import safe_getattr, safe_hasattr
  from .hub_interface import RobertaHubInterface
label_desc: Use image node_id 1 for calling a global function with example usage: base_architecture(args) without return types