code: def __init__(self, arg1, arg2, name="MultibitKernel"):
      super(MultibitKernel, self).__init__(
          [arg1], as_numpy=False, name=name
      )
      # save the input bit map for later
      self.bit_map = arg2
      # create an input variable and a function object for the forward propagated function
      self.action, self.actionArg = self.multiFunc(arg1)
      # create a binary input gradient function, two input variables and the function object. gradroot is the incoming
      # gradient from stages down the pipeline and gradarg is the argument we need to do our new gradient. In our
      # case, we need the inputs to the forward function.
      self.grad, self.gradArg, self.gradRoot = self.gradFunc(arg1)
  
imports: import numpy
  import cntk
  from cntk.ops.functions import UserFunction
label_desc: Use image node_id 1 to create a new MultibitKernel object from inherited base classes: UserFunction with example: obj = MultibitKernel(arg1, arg2, name)