code: def _get_other_var(self, X_feat, X_seq, variable="y_pred"):
          
          if self.is_trained() is False:
              print("Model not fitted yet. Use object.fit() to fit the model.")
              return
  
          # input check:
          assert X_seq.shape[0] == X_feat.shape[0]
  
          # TODO - check this
          # sequence can be wider or thinner?
          # assert self._param["seq_length"] == X_seq.shape[2]
          assert self._param["n_add_features"] == X_feat.shape[1]
  
          # setup graph and variables
          graph = tf.Graph()
          var = self._convert_to_var(graph, self._var_res)
          other_var = self._build_graph(graph, var)
          with tf.Session(graph=graph) as sess:
              sess.run(other_var["init"])
              # predict
              y = self._predict_in_session(sess, other_var, X_feat, X_seq, variable)
  
          return y
docs: Get the value of a variable from other_vars (from a tf-graph)