def METHOD_NAME(self):
    """Test model with embeddings."""
    input_data = {
        "x": tf.constant(
            np.array(np.random.random_sample((20)), dtype=np.int32)
        )
    }
    class EmbeddingModel(keras.Model):
        def __init__(self):
            super().__init__()
            self.shared_weights = self.add_weight(
                "weights",
                shape=(2000, 300),
                dtype=tf.float32,
                initializer=tf.compat.v1.random_normal_initializer(
                    mean=0.0, stddev=300 ** (-0.5)
                ),
            )
        @tf.function(
            input_signature=[tf.TensorSpec(shape=(20), dtype=tf.int32)]
        )
        def func(self, x):
            return tf.gather(self.shared_weights, x)
    model = EmbeddingModel()
    root, output_func = self._freezeModel(model.func)
    self._testConvertedFunction(root, root.f, output_func, input_data)