Custom py_func with gradient support

def py_func_grad(func, inp, Tout, stateful=True, name=None, grad=None):
  """Custom py_func with gradient support

  """
  # Need to generate a unique name to avoid duplicates:
  rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))

  tf.RegisterGradient(rnd_name)(grad)
  g = tf.get_default_graph()
  with g.gradient_override_map({"PyFunc": rnd_name,
                                "PyFuncStateless": rnd_name}):
    return tf.py_func(func, inp, Tout, stateful=stateful, name=name)