code: def assert_zero_shift(order, regularization, num_boundary_points):
      """Check that warping with zero displacements doesn't change the
      image."""
      batch_size = 1
      image_height = 4
      image_width = 4
      channels = 3
  
      image = np.random.uniform(
          size=[batch_size, image_height, image_width, channels]
      )
  
      input_image = tf.constant(np.float32(image))
  
      control_point_locations = [[1.0, 1.0], [2.0, 2.0], [2.0, 1.0]]
      control_point_locations = tf.constant(
          np.float32(np.expand_dims(control_point_locations, 0))
      )
  
      control_point_displacements = np.zeros(
          control_point_locations.shape.as_list()
      )
      control_point_displacements = tf.constant(
          np.float32(control_point_displacements)
      )
  
      (warped_image, _) = sparse_image_warp(
          input_image,
          control_point_locations,
          control_point_locations + control_point_displacements,
          interpolation_order=order,
          regularization_weight=regularization,
          num_boundary_points=num_boundary_points,
      )
  
      np.testing.assert_allclose(
          warped_image, input_image, rtol=1e-6, atol=1e-6
      )
  
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 2 for calling a global function with example usage: assert_zero_shift(order, regularization, num_boundary_points) without return types