class_docstr: Recurrent NStepLSTM language model.
  
  Generate captions given features extracted from images.
docstr: Batch of image features to LSTM states and hidden representations.
  
          
docstr_tok: ["Batch","of","image","features","to","LSTM","states","and","hidden","representations","."]
code: def reset(self, img_feats):
      """Batch of image features to LSTM states and hidden representations."""
      h = self.embed_img(img_feats)
      h = F.split_axis(h, h.shape[0], axis=0)
      hx, cx, ys = self.lstm(None, None, h)
      return hx, cx, ys
  
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 NStepLSTMLanguageModel obj's underlying member method code with example usage: obj.reset(img_feats) and returns: hx,  cx,  ys