code: def xstep(self):
          
  
          self.YU1[:] = self.Y1 - self.U1
          self.ZSf = np.conj(self.Zf) * (self.Sf + sl.rfftn(
              self.YU1, None, self.cri.axisN))
          rho = self.rho
          self.rho = 1.0
          super(ConvCnstrMODMaskDcpl_Consensus, self).xstep()
          self.rho = rho
docs: The xstep of the baseline consensus class from which this
          class is derived is re-used to implement the xstep of the
          modified algorithm by replacing ``self.ZSf``, which is constant
          in the baseline algorithm, with a quantity derived from the
          additional variables ``self.Y1`` and ``self.U1``. It is also
          necessary to set the penalty parameter to unity for the duration
          of the x step.