import tensorflow as tf

        grads = [tf.random_uniform([10, 10], -1., 1.) for _ in range(5)]
        grads_and_vars = list(zip(grads, range(5)))

        hparams = {
            "type": "clip_by_global_norm",
            "kwargs": {
                "clip_norm": 0.1
            }
        }
        gn_grad_clip_fn = opt.get_gradient_clip_fn(hparams)
        gn_grads_and_vars = gn_grad_clip_fn(grads_and_vars)
        gn_grads, _ = zip(*gn_grads_and_vars)
        gn_grads_true, _ = tf.clip_by_global_norm(
            grads, hparams["kwargs"]["clip_norm"])

        hparams = {
            "type": "clip_by_value",
            "kwargs": {
                "clip_value_min": -0.01,
                "clip_value_max": 0.01
            }
        }
        v_grad_clip_fn = opt.get_gradient_clip_fn(hparams)
        v_grads_and_vars = v_grad_clip_fn(grads_and_vars)
