code: def init_vars(self, S, dimK):
          
  
          Nv = S.shape[0:self.dimN]
          if self.cri is None or Nv != self.cri.Nv:
              self.cri = cr.CDU_ConvRepIndexing(self.dsz, S, dimK, self.dimN)
              if self.opt['CUDA_CBPDN']:
                  if self.cri.Cd > 1 or self.cri.Cx > 1:
                      raise ValueError('CUDA CBPDN solver can only be used for '
                                       'single channel problems')
                  if self.cri.K > 1:
                      raise ValueError('CUDA CBPDN solver can not be used with '
                                       'mini-batches')
              self.Df = sl.pyfftw_byte_aligned(sl.rfftn(self.D, self.cri.Nv,
                                                        self.cri.axisN))
              self.Gf = sl.pyfftw_empty_aligned(self.Df.shape, self.Df.dtype)
              self.Z = sl.pyfftw_empty_aligned(self.cri.shpX, self.dtype)
          else:
              self.Df[:] = sl.rfftn(self.D, self.cri.Nv, self.cri.axisN)
docs: Initalise variables required for sparse coding and dictionary
          update for training data `S`.