code: def xstep(self, S, W, lmbda, dimK):
          
  
          if self.opt['CUDA_CBPDN']:
              Z = cuda.cbpdnmsk(self.D.squeeze(), S[..., 0], W.squeeze(), lmbda,
                                self.opt['CBPDN'])
              Z = Z.reshape(self.cri.Nv + (1, 1, self.cri.M,))
              self.Z[:] = np.asarray(Z, dtype=self.dtype)
              self.Zf = sl.rfftn(self.Z, self.cri.Nv, self.cri.axisN)
              self.Sf = sl.rfftn(S.reshape(self.cri.shpS), self.cri.Nv,
                                 self.cri.axisN)
              self.xstep_itstat = None
          else:
              # Create X update object (external representation is expected!)
              xstep = cbpdn.ConvBPDNMaskDcpl(self.D.squeeze(), S, lmbda, W,
                                             self.opt['CBPDN'], dimK=dimK,
                                             dimN=self.cri.dimN)
              xstep.solve()
              self.Sf = sl.rfftn(S.reshape(self.cri.shpS), self.cri.Nv,
                                 self.cri.axisN)
              self.setcoef(xstep.getcoef())
              self.xstep_itstat = xstep.itstat[-1] if xstep.itstat else None
docs: Solve CSC problem for training data `S`.