def METHOD_NAME():
    g = tf.Graph()
    with g.as_default():
        x = tf.placeholder(tf.float32, shape=())
        # It turns out that tf.assert() creates a large and complex subgraph if
        # you capture a variable as part of the error message. So we need to
        # test that, too.
        assert_op = tf.Assert(tf.less_equal(x, x), ["it failed", x])
        with tf.Session() as sess:
            x_value = np.random.rand()
            assert sess.run(assert_op, feed_dict={x: x_value}) is None
        # TODO: The frontend converter notes the output of
        # the graph as a boolean, which is not correct - as you can see above,
        # TF believes that the value of this graph is None.
        np.testing.assert_allclose(True, run_relay(g, None, x_value).numpy())