class_docstr: Recurrent LSTM language model.
  
  Generate captions given features extracted from images.
docstr: Batch of image features to hidden representations.
  
  Also, reset and then update the internal state of the LSTM.
docstr_tok: ["Batch","of","image","features","to","hidden","representations",".","Also",",","reset","and","then","update","the","internal","state","of","the","LSTM","."]
code: def reset(self, img_feats):
      """Batch of image features to hidden representations.
  
      Also, reset and then update the internal state of the LSTM.
      """
      self.lstm.reset_state()
      h = self.embed_img(img_feats)
      h = self.lstm(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 4 for calling the LSTMLanguageModel obj's underlying member method code with example usage: obj.reset(img_feats) and returns: h