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","`"]
code: def update_dropout(self, dropout, attention_dropout=None):
      self.dropout.p = dropout
      self.rnn.dropout.p = dropout
      self.embeddings.update_dropout(dropout)
  
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 4 for calling the InputFeedRNNDecoder obj's underlying member method code with example usage: obj.update_dropout(dropout, attention_dropout) without return types