class_docstr: Decoder with general structure of Chen et al., The Best of Both Worlds:
  Combining Recent Advances in Neural Machine Translation, 2018.
  https://arxiv.org/abs/1804.09849
class_docstr_tok: ["Decoder","with","general","structure","of","Chen","et","al.",",","The","Best","of","Both","Worlds",":","Combining","Recent","Advances","in","Neural","Machine","Translation",",","2018.","https",":","\/\/arxiv.org\/abs\/1804.09849"]
docstr: Concat latent code, if available in encoder_out, which is the
  case in subclass.
docstr_tok: ["Concat","latent","code",",","if","available","in","encoder_out",",","which","is","the","case","in","subclass","."]
code: def _concat_latent_code(self, x, encoder_out):
      """Concat latent code, if available in encoder_out, which is the
      case in subclass.
      """
      return x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, transformer
  from fairseq.modules import MultiheadAttention
  from pytorch_translate import multilingual_model, transformer, vocab_reduction
  from pytorch_translate.utils import torch_find
label_desc: Use image node_id 6 for calling the HybridRNNDecoder obj's underlying member method code with example usage: obj._concat_latent_code(x, encoder_out) and returns: x