Cleanup

    self.b_list.append(backend.zeros(shape=[

        self.n_outputs,

    ]))

    #with tf.init_scope():

    #  graph_features_initial = tf.zeros((self.max_atoms * self.batch_size,

    #                                     self.max_atoms + 1, self.n_graph_feat))

    #  self.graph_features = tf.Variable(graph_features_initial, trainable=False)

    self.built = True

  def call(self, inputs):

    n_atoms = tf.squeeze(inputs[4])

    dropout_switch = tf.squeeze(inputs[5])

    #with tf.init_scope():

    #  # initialize graph features for each graph

    #  graph_features_initial = tf.zeros((self.max_atoms * self.batch_size,

    #                                     self.max_atoms + 1, self.n_graph_feat))

    #  # initialize graph features for each graph

    #  # another row of zeros is generated for padded dummy atoms

    #  #graph_features = tf.Variable(graph_features_initial, trainable=False)

    #  self.graph_features.assign(graph_features_initial)

    graph_features = tf.zeros((self.max_atoms * self.batch_size,

                               self.max_atoms + 1, self.n_graph_feat))

      # index for targe atoms

      target_index = tf.stack([tf.range(n_atoms), parents[:, count, 0]], axis=1)

      target_index = tf.boolean_mask(target_index, mask)

      # update the graph features for target atoms

      #self.graph_features = tf.compat.v1.scatter_nd_update(

      #    self.graph_features, target_index, batch_outputs)

      #self.graph_features.assign_add(

      #    tf.compat.v1.scatter_nd_update(self.graph_features, target_index,

      #                                   batch_outputs))

      graph_features = tf.tensor_scatter_nd_update(graph_features, target_index,

                                                   batch_outputs)

    return batch_outputs

    # Fit trained model

    model.fit(dataset, nb_epoch=1)

    #batch_size = 50

    #model = GraphConvModel(

    #    len(tasks), batch_size=batch_size, mode='classification')

    #model.fit(dataset, nb_epoch=10)

    #scores = model.evaluate(dataset, [metric], transformers)

    #assert scores['mean-roc_auc_score'] >= 0.9

    ])

    ## TODO(rbharath): What is the shape of outputs supposed to be?

    ## I'm getting (7, 30) here. Where does 7 come from??

    #print("outputs.shape")

    #print(outputs.shape)

  def test_DAG_gather(self):

    """Test invoking DAGGather."""