code: def test_that_backprop_runs():
      """Making sure the gradients can be computed."""
      batch_size = 1
      image_height = 9
      image_width = 12
      image = tf.Variable(
          np.random.uniform(
              size=[batch_size, image_height, image_width, 3]
          ),
          dtype=tf.float32,
      )
      control_point_locations = [[3.0, 3.0]]
      control_point_locations = tf.constant(
          np.float32(np.expand_dims(control_point_locations, 0))
      )
      control_point_displacements = [[0.25, -0.5]]
      control_point_displacements = tf.constant(
          np.float32(np.expand_dims(control_point_displacements, 0))
      )
  
      with tf.GradientTape() as t:
          warped_image, _ = sparse_image_warp(
              image,
              control_point_locations,
              control_point_locations + control_point_displacements,
              num_boundary_points=3,
          )
  
      gradients = t.gradient(warped_image, image).numpy()
      assert np.sum(np.abs(gradients)) != 0
  
file_path: addons/tensorflow_addons/image/tests/sparse_image_warp_test.py
imports: from collections import namedtuple
  import numpy
  import pytest
  import tensorflow
  from tensorflow_addons.image import sparse_image_warp
  from tensorflow_addons.image.sparse_image_warp import _get_boundary_locations
  from tensorflow_addons.image.sparse_image_warp import _get_grid_locations
  from tensorflow_addons.utils.resource_loader import get_path_to_datafile
label_desc: Use image node_id 9 for calling a global function with example usage: test_that_backprop_runs() without return types