def METHOD_NAME():
    g = tf.Graph()
    shape = (1, 2)
    with g.as_default():
        x = tf.placeholder(tf.float32, shape=shape, name="input")
        assert_op = tf.Assert(tf.reduce_all(tf.less_equal(x, x)), ["it failed"])
        with tf.Session() as sess:
            x_value = np.random.rand(*shape)
            assert sess.run(assert_op, feed_dict={x: x_value}) is None
        # In TVM, tf.assert is converted to a no-op which is actually a 0,
        # though it should probably be none or an empty tuple.
        #
        # ToDo: It appears that the frontend converter gets confused here and
        # entirely eliminates all operands from main(). Likely because x <= x
        # is always true, so the placeholder can be eliminated. But TF doesn't
        # do that, it's happening in Relay, and that optimization shouldn't
        # affect the arity of the main function. We should have to pass in
        # x_value here.
        np.testing.assert_allclose(0, run_relay(g, {"input": shape}).numpy())