code: def fconv_lm_dauphin_wikitext103(args):
      layers = "[(850, 6)] * 3"
      layers += " + [(850, 1)] * 1"
      layers += " + [(850, 5)] * 4"
      layers += " + [(850, 1)] * 1"
      layers += " + [(850, 4)] * 3"
      layers += " + [(1024, 4)] * 1"
      layers += " + [(2048, 4)] * 1"
      args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 280)
      args.decoder_layers = getattr(args, "decoder_layers", layers)
      args.decoder_attention = getattr(
          args, "decoder_attention", "False"
      )
      args.adaptive_softmax_cutoff = getattr(
          args, "adaptive_softmax_cutoff", "10000,20000,200000"
      )
      base_lm_architecture(args)
  
file_path: fairseq/fairseq/models/fconv_lm.py
imports: from fairseq import utils
  from fairseq.models import FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.models.fconv import FConvDecoder
  from fairseq.utils import safe_hasattr
label_desc: Use image node_id 2 for calling a global function with example usage: fconv_lm_dauphin_wikitext103(args) without return types