message: Eager/g3doc: Gradients with respect to constants are None and not 0.
  Same behavior as tf.gradients() for graphs.
  Some discussion of this choice in
diff: @@ -273,9 +273,9 @@ assert 6 == df(3.)[0].numpy()
  d2f = tfe.gradients_function(lambda x: df(x)[0])
  assert 2 == d2f(3.)[0].numpy()
  -# Third order derivative.
  +# Third order derivative: Will be None
  d3f = tfe.gradients_function(lambda x : d2f(x)[0])
  -assert 0 == d3f(3.)[0].numpy()
  +assert None == d3f(3.)[0]
  ```
  These functions can be used to train models. For example, consider the following
  