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: Initialize with latent code if available otherwise zeros.
docstr_tok: ["Initialize","with","latent","code","if","available","otherwise","zeros","."]
code: def _init_hidden(self, encoder_out, batch_size):
      """Initialize with latent code if available otherwise zeros."""
      return torch.zeros([1, batch_size, self.lstm_units])
  
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 5 for calling the HybridRNNDecoder obj's underlying member method code with example usage: obj._init_hidden(encoder_out, batch_size) and returns: torch