def METHOD_NAME(self):
    X = np.random.randn(3, 6, 4)
    X[1, 4:] = 0
    X_lengths = [6, 4, 6]
    rnn_hidden_size = 5
    rnn_type = "ltsm1"
    if rnn_type == "lstm":
        cell = tf.contrib.rnn.BasicLSTMCell(num_units=rnn_hidden_size, state_is_tuple=True)
    else:
        cell = tf.contrib.rnn.GRUCell(num_units=rnn_hidden_size)
    outputs, last_states = tf.nn.dynamic_rnn(cell=cell, dtype=tf.float64, sequence_length=X_lengths, inputs=X)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        o1, s1 = sess.run([outputs, last_states])
        rs = Helper.analysis_rnn_model(sess.graph.as_graph_def())
        self.assertEqual(len(rs.keys()), 2)