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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def init_vars(self, S, dimK):
 """Initalise variables required for sparse coding and dictionary update for training data `S`."""


        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)