class_docstr: End-To-End Memory Network.
class_docstr_tok: ["End-To-End","Memory","Network","."]
docstr: Predicts answers as one-hot encoding.
  
  Args:
      stories: Tensor (None, memory_size, sentence_size)
      queries: Tensor (None, sentence_size)
  
  Returns:
      answers: Tensor (None, vocab_size)
docstr_tok: ["Predicts","answers","as","one-hot","encoding",".","Args",":","stories",":","Tensor","(","None",",","memory_size",",","sentence_size",")","queries",":","Tensor","(","None",",","sentence_size",")","Returns",":","answers",":","Tensor","(","None",",","vocab_size",")"]
code: def predict(self, stories, queries, cands):
      """Predicts answers as one-hot encoding.
  
      Args:
          stories: Tensor (None, memory_size, sentence_size)
          queries: Tensor (None, sentence_size)
  
      Returns:
          answers: Tensor (None, vocab_size)
      """
      feed_dict = {
          self._stories: stories,
          self._queries: queries,
          self._cands: cands,
      }
      return self._sess.run(self.predict_op, feed_dict=feed_dict)
  
file_path: nlp-architect/examples/memn2n_dialogue/memn2n_dialogue.py
imports: from __future__ import absolute_import
  from __future__ import division
  import tensorflow
  from six.moves import range
label_desc: Use image node_id 6 for calling the MemN2N_Dialog obj's underlying member method code with example usage: obj.predict(stories, queries, cands) and returns: self