class_docstr: End-To-End Memory Network.
class_docstr_tok: ["End-To-End","Memory","Network","."]
code: def _build_vars(self):
      with tf.variable_scope(self._name):
          nil_word_slot = tf.zeros([1, self._embedding_size])
          A = tf.concat(
              axis=0,
              values=[
                  nil_word_slot,
                  self._init(
                      [self._vocab_size - 1, self._embedding_size]
                  ),
              ],
          )
          W = tf.concat(
              axis=0,
              values=[
                  nil_word_slot,
                  self._init(
                      [self._vocab_size - 1, self._embedding_size]
                  ),
              ],
          )
  
          self.LUT_A = tf.Variable(A, name="LUT_A")
          self.LUT_W = tf.Variable(W, name="LUT_W")
  
          # Dont use projection for layerwise weight sharing
          self.R_proj = tf.Variable(
              self._init([self._embedding_size, self._embedding_size]),
              name="R_proj",
          )
  
      self._nil_vars = set([self.LUT_A.name, self.LUT_W.name])
  
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 3 for calling the MemN2N_Dialog obj's underlying member method code with example usage: obj._build_vars() without return types