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"]
code: def forward(
      self,
      prev_output_tokens,
      encoder_out,
      incremental_state=None,
      possible_translation_tokens=None,
      timestep=None,
  ):
      x, prev_output_tokens = self._embed_prev_outputs(
          prev_output_tokens=prev_output_tokens,
          incremental_state=incremental_state,
      )
      return self._forward_given_embeddings(
          embed_out=x,
          prev_output_tokens=prev_output_tokens,
          encoder_out=encoder_out,
          incremental_state=incremental_state,
          possible_translation_tokens=possible_translation_tokens,
          timestep=timestep,
      )
  
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 9 for calling the HybridRNNDecoder obj's underlying member method code with example usage: obj.forward(prev_output_tokens, encoder_out, incremental_state, possible_translation_tokens, timestep) and returns: self