def METHOD_NAME(checkpoint, alpha, num_styles):
  """Export a image stylization checkpoint to SavedModel format."""
  saved_model_dir = tempfile.mkdtemp()
  with tf.Graph().as_default(), tf.Session() as sess:
    # Define input tensor as placeholder to allow export
    input_image_tensor = tf.placeholder(
        tf.float32, shape=(1, None, None, 3), name='input_image')
    weights_tensor = tf.placeholder(
        tf.float32, shape=num_styles, name='style_weights')
    # Load the graph definition from Magenta
    stylized_image_tensor = model.transform(
        input_image_tensor,
        alpha=alpha,
        normalizer_fn=ops.weighted_instance_norm,
        normalizer_params={
            'weights': weights_tensor,
            'num_categories': FLAGS.num_styles,
            'center': True,
            'scale': True
        })
    # Load model weights from downloaded checkpoint file
    _load_checkpoint(sess, checkpoint)
    # Write SavedModel for serving or conversion to TF Lite
    tf.saved_model.simple_save(
        sess,
        saved_model_dir,
        inputs={
            input_image_tensor.name: input_image_tensor,
            weights_tensor.name: weights_tensor
        },
        outputs={'stylized_image': stylized_image_tensor})
  return saved_model_dir