code: def multilingual_small(args):
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 512
      )
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 4
      )
      args.encoder_layers = getattr(args, "encoder_layers", 3)
      args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
      args.decoder_ffn_embed_dim = getattr(
          args, "decoder_ffn_embed_dim", 512
      )
      args.decoder_attention_heads = getattr(
          args, "decoder_attention_heads", 4
      )
      args.decoder_layers = getattr(args, "decoder_layers", 3)
      base_multilingual_architecture(args)
  
file_path: fairseq/examples/emotion_conversion/fairseq_models/__init__.py
imports: from fairseq import utils
  from fairseq.models import FairseqMultiModel, register_model, register_model_architecture
  from fairseq.models.transformer import Embedding, base_architecture
  from fairseq.models.multilingual_transformer import MultilingualTransformerModel, base_multilingual_architecture
  from fairseq.utils import safe_hasattr
  from collections import OrderedDict
label_desc: Use image node_id 2 for calling a global function with example usage: multilingual_small(args) without return types