code: def gradFunc(self, arg):
      # create an input variable corresponding the inputs of the forward prop function
      gradIn = C.input(shape=arg.shape, dynamic_axes=arg.dynamic_axes)
      # create an input variable for the gradient passed from the next stage
      gradRoot = C.input(shape=arg.shape, dynamic_axes=arg.dynamic_axes)
      # first step is to take absolute value of input arg
      signGrad = C.abs(gradIn)
      # then compare its magnitude to 1
      signGrad = C.less_equal(signGrad, 1)
      # finish by multiplying this result with the input gradient
      return C.element_times(gradRoot, signGrad), gradIn, gradRoot
  
imports: import numpy
  import cntk
  from cntk.ops.functions import UserFunction
label_desc: Use image node_id 3 for calling the SignWithEstimation obj's underlying member method code with example usage: obj.gradFunc(arg) and returns: C,  gradIn,  gradRoot