code: def base_architecture(args):
      # default architecture
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
      args.encoder_layers = getattr(args, "encoder_layers", 1)
      args.encoder_hidden_dim = getattr(args, "encoder_hidden_dim", 512)
      args.encoder_bidirectional = getattr(
          args, "encoder_bidirectional", False
      )
      args.encoder_dropout_in = getattr(
          args, "encoder_dropout_in", args.dropout
      )
      args.encoder_dropout_out = getattr(
          args, "encoder_dropout_out", args.dropout
      )
      args.encoder_pretrained_embed = getattr(
          args, "encoder_pretrained_embed", None
      )
      args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
      args.decoder_layers = getattr(args, "decoder_layers", 1)
      args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 512)
      args.decoder_pretrained_embed = getattr(
          args, "decoder_pretrained_embed", None
      )
      args.decoder_out_embed_dim = getattr(
          args, "decoder_out_embed_dim", 512
      )
      args.decoder_out_pretrained_embed = getattr(
          args, "decoder_out_pretrained_embed", None
      )
      args.attention_type = getattr(args, "attention_type", "dot")
      args.attention_heads = getattr(args, "attention_heads", 8)
      args.first_layer_attention = getattr(
          args, "first_layer_attention", False
      )
      args.decoder_dropout_in = getattr(
          args, "decoder_dropout_in", args.dropout
      )
      args.decoder_dropout_out = getattr(
          args, "decoder_dropout_out", args.dropout
      )
      args.averaging_encoder = getattr(args, "averaging_encoder", False)
      args.encoder_freeze_embed = getattr(
          args, "encoder_freeze_embed", False
      )
      args.encoder_normalize_embed = getattr(
          args, "encoder_normalize_embed", False
      )
      args.decoder_freeze_embed = getattr(
          args, "decoder_freeze_embed", False
      )
      args.ngram_decoder = getattr(args, "ngram_decoder", None)
      args.multi_encoder = getattr(args, "multi_encoder", None)
      args.multi_decoder = getattr(args, "multi_decoder", None)
      args.multi_decoder_is_lm = getattr(
          args, "multi_decoder_is_lm", None
      )
      args.multiling_encoder_lang = getattr(
          args, "multiling_encoder_lang", None
      )
      args.multi_model_training_schedule = getattr(
          args, "multi_model_training_schedule", "complete"
      )
      args.multi_model_fixed_weights = getattr(
          args, "multi_model_fixed_weights", None
      )
      args.cell_type = getattr(args, "cell_type", "lstm")
      args.ngram_activation_type = getattr(
          args, "ngram_activation_type", "relu"
      )
      vocab_reduction.set_arg_defaults(args)
      args.sequence_lstm = getattr(args, "sequence_lstm", False)
      args.decoder_tie_embeddings = getattr(
          args, "decoder_tie_embeddings", False
      )
      args.encoder_pretrained_embed = getattr(
          args, "encoder_pretrained_embed", None
      )
      args.decoder_pretrained_embed = getattr(
          args, "decoder_pretrained_embed", None
      )
      args.decoder_out_pretrained_embed = getattr(
          args, "decoder_out_pretrained_embed", None
      )
      args.out_embed_norm = getattr(args, "out_embed_dim", None)
      args.att_weighted_src_embeds = getattr(
          args, "att_weighted_source_embeds", False
      )
      args.att_weighted_activation_type = getattr(
          args, "att_weighted_activation_type", "tanh"
      )
      args.adaptive_softmax_cutoff = getattr(
          args, "adaptive_softmax_cutoff", None
      )
      args.language_model_only = getattr(
          args, "language_model_only", False
      )
      args.encoder_context_embed = getattr(
          args, "encoder_context_embed", False
      )
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.onnx.operators
  from fairseq import options, utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.modules import AdaptiveSoftmax
  from pytorch_translate import attention, rnn_cell, utils, vocab_reduction
  from pytorch_translate.common_layers import ContextEmbedding, DecoderWithOutputProjection, Embedding, Linear, RNNLayer, VariableTracker
  from pytorch_translate.data import data
  from pytorch_translate.multi_model import MultiDecoder, MultiEncoder
  from pytorch_translate.multilingual import MultilingualDecoder, MultilingualEncoder
  from pytorch_translate.ngram import NGramDecoder
  from pytorch_translate.semi_supervised import SemiSupervisedModel
  from pytorch_translate.utils import maybe_cat, maybe_cuda, torch_find
  from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence, pad_packed_sequence
label_desc: Use image node_id 2 for calling a global function with example usage: base_architecture(args) without return types