code: def backward(self, state, root_gradients):
      # first extract the value passed by the state, in this case the argument to forward.
      val = state
      # now perform a forward on our gradient function to compute the proper outputs. Map val to gradArg and the
      # root gradient to gradRoot to properly set up the binary gradient inputs.
      _, output_values = self.grad.forward(
          {self.gradArg: val, self.gradRoot: root_gradients},
          self.grad.outputs,
          device=state.device(),
          as_numpy=False,
      )
      # return the proper output
      return output_values[self.grad.output]
  
imports: from cntk import *
  from cntk.ops.functions import UserFunction
  import numpy
  import cntk
label_desc: Use image node_id 5 for calling the Sign obj's underlying member method code with example usage: obj.backward(state, root_gradients) and returns: output_values