code: def base_architecture(args):
      args.dropout = getattr(args, "dropout", 0.1)
      args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
      args.decoder_embed_path = getattr(
          args, "decoder_embed_path", None
      )
      args.decoder_hidden_size = getattr(
          args, "decoder_hidden_size", args.decoder_embed_dim
      )
      args.decoder_layers = getattr(args, "decoder_layers", 1)
      args.decoder_out_embed_dim = getattr(
          args, "decoder_out_embed_dim", 512
      )
      args.decoder_attention = getattr(args, "decoder_attention", "0")
      args.decoder_dropout_in = getattr(
          args, "decoder_dropout_in", args.dropout
      )
      args.decoder_dropout_out = getattr(
          args, "decoder_dropout_out", args.dropout
      )
      args.share_decoder_input_output_embed = getattr(
          args, "share_decoder_input_output_embed", False
      )
      args.adaptive_softmax_cutoff = getattr(
          args, "adaptive_softmax_cutoff", "10000,50000,200000"
      )
      args.residuals = getattr(args, "residuals", False)
  
file_path: fairseq/fairseq/models/lstm_lm.py
imports: from fairseq import utils
  from fairseq.models import FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.models.lstm import Embedding, LSTMDecoder
label_desc: Use image node_id 1 for calling a global function with example usage: base_architecture(args) without return types