code: def reconstruct(self, data, graph=None):
          
          g = graph if graph is not None else self.tf_graph
  
          with g.as_default():
              with tf.Session() as self.tf_session:
                  self.tf_saver.restore(self.tf_session, self.model_path)
                  feed = {self.input_data: data, self.keep_prob: 1}
                  return self.reconstruction.eval(feed)
docs: Reconstruct data according to the model.
  
          Parameters
          ----------
  
          data : array_like, shape (n_samples, n_features)
              Data to transform.
  
          graph : tf.Graph, optional (default = None)
              Tensorflow Graph object
  
          Returns
          -------
          array_like, transformed data