class_docstr: Recurrent LSTM language model.
  
  Generate captions given features extracted from images.
docstr: Batch of word tokens to word tokens.
  
  Predict the next set of tokens given previous tokens.
docstr_tok: ["Batch","of","word","tokens","to","word","tokens",".","Predict","the","next","set","of","tokens","given","previous","tokens","."]
code: def step(self, x):
      """Batch of word tokens to word tokens.
  
      Predict the next set of tokens given previous tokens.
      """
      h = self.embed_word(x)
      h = self.lstm(F.dropout(h, ratio=self.dropout_ratio))
      h = self.out_word(F.dropout(h, ratio=self.dropout_ratio))
      return h
  
imports: import numpy
  import chainer
  from chainer import functions
  from chainer import initializers
  from chainer import links
  from chainer import reporter
  from chainer import Variable
label_desc: Use image node_id 5 for calling the LSTMLanguageModel obj's underlying member method code with example usage: obj.step(x) and returns: h