code: def build_single_encoder(args, src_dict, embed_tokens):
      if args.language_model_only:
          return DummyEncoder(src_dict, num_layers=args.encoder_layers)
      if args.sequence_lstm:
          encoder_class = LSTMSequenceEncoder
      else:
          encoder_class = RNNEncoder
      return encoder_class(
          src_dict,
          embed_tokens=embed_tokens,
          embed_dim=args.encoder_embed_dim,
          cell_type=args.cell_type,
          num_layers=args.encoder_layers,
          hidden_dim=args.encoder_hidden_dim,
          dropout_in=args.encoder_dropout_in,
          dropout_out=args.encoder_dropout_out,
          residual_level=args.residual_level,
          bidirectional=bool(args.encoder_bidirectional),
          left_pad=args.left_pad_source,
          encoder_context_embed=args.encoder_context_embed,
      )
  
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 3 for calling the RNNModel obj's underlying member method code with example usage: obj.build_single_encoder(args, src_dict, embed_tokens) and returns: encoder_class, DummyEncoder