code: def test_gradients_exist():
      """Check that backprop can run.
  
      The correctness of the gradients is assumed, since the forward
      propagation is tested to be correct and we only use built-in tf
      ops. However, we perform a simple test to make sure that
      backprop can actually run.
      """
      batch_size, height, width, num_channels = [4, 5, 6, 7]
      image_shape = [batch_size, height, width, num_channels]
      image = tf.random.normal(image_shape)
      flow_shape = [batch_size, height, width, 2]
      flows = tf.Variable(
          tf.random.normal(shape=flow_shape) * 0.25, dtype=tf.float32
      )
  
      with tf.GradientTape() as t:
          interp = dense_image_warp(image, flows)
  
      grads = t.gradient(interp, flows).numpy()
      assert np.sum(np.abs(grads)) != 0
  
file_path: addons/tensorflow_addons/image/tests/dense_image_warp_test.py
imports: import pytest
  import math
  import numpy
  import tensorflow
  from tensorflow_addons.image import dense_image_warp
  from tensorflow_addons.image import interpolate_bilinear
label_desc: Use image node_id 7 for calling a global function with example usage: test_gradients_exist() without return types