def _py_func_with_gradient(func, inp, Tout, stateful=True, name=None,
                           grad_func=None):
  """
  PyFunc defined as given by Tensorflow
  :param func: Custom Function
  :param inp: Function Inputs
  :param Tout: Ouput Type of out Custom Function
  :param stateful: Calculate Gradients when stateful is True
  :param name: Name of the PyFunction
  :param grad: Custom Gradient Function
  :return:
  """
  # Generate random name in order to avoid conflicts with inbuilt names
  rnd_name = 'PyFuncGrad-' + '%0x' % getrandbits(30 * 4)

  # Register Tensorflow Gradient
  tf.RegisterGradient(rnd_name)(grad_func)

  # Get current graph
  g = tf.get_default_graph()

  # Add gradient override map
  with g.gradient_override_map({"PyFunc": rnd_name,
                                "PyFuncStateless": rnd_name}):
    return tf.py_func(func, inp, Tout, stateful=stateful, name=name)