class_docstr: Input feeding based decoder.
  
  See :class:`~onmt.decoders.decoder.RNNDecoderBase` for options.
  
  Based around the input feeding approach from
  "Effective Approaches to Attention-based Neural Machine Translation"
  :cite:`Luong2015`
class_docstr_tok: ["Input","feeding","based","decoder",".","See",":","class",":","`","~onmt.decoders.decoder.RNNDecoderBase","`","for","options",".","Based","around","the","input","feeding","approach","from","''","Effective","Approaches","to","Attention-based","Neural","Machine","Translation","''",":","cite",":","`","Luong2015","`"]
docstr: Using input feed by concatenating input with attention vectors.
docstr_tok: ["Using","input","feed","by","concatenating","input","with","attention","vectors","."]
code: def _input_size(self):
      """Using input feed by concatenating input with attention vectors."""
      return self.embeddings.embedding_size + self.hidden_size
  
imports: import torch
  import torch.nn
  from onmt.modules.stacked_rnn import StackedLSTM, StackedGRU
  from onmt.modules import context_gate_factory, GlobalAttention
  from onmt.utils.rnn_factory import rnn_factory
label_desc: Use image node_id 3 for calling the InputFeedRNNDecoder obj's underlying member method code with example usage: obj._input_size() and returns: unknown