code: def roberta_large_architecture(args):
      args.encoder_layers = safe_getattr(args, "encoder_layers", 24)
      args.encoder_embed_dim = safe_getattr(
          args, "encoder_embed_dim", 1024
      )
      args.encoder_ffn_embed_dim = safe_getattr(
          args, "encoder_ffn_embed_dim", 4096
      )
      args.encoder_attention_heads = safe_getattr(
          args, "encoder_attention_heads", 16
      )
      base_architecture(args)
  
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 4 for calling a global function with example usage: roberta_large_architecture(args) without return types