code: def test_can_differentiate_scan(self):
      n = 2**4 - 1
      x = self._maybe_static(tf.ones(n, dtype=tf.float64))
  
      def fn(x):
          y = scan_associative(operator.add, x, max_num_levels=8)
          return tf.tensordot(y, y, 1)
  
      _, dz_dx = gradient.value_and_gradient(fn, x)
  
      k = tf.range(n, dtype=tf.float64)
      # Exact result (n + k + 1) * (n - k) computed in Mathematica.
      self.assertAllClose(dz_dx, (n + k + 1) * (n - k))
  
imports: import collections
  import functools
  import operator
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.scan_associative import scan_associative
label_desc: Use image node_id 10 for calling the _ScanAssociativeTest obj's underlying member method code with example usage: obj.test_can_differentiate_scan() and returns: tf