code: def _compute_r2l2(self, tau, return_l=False):
          r
          l_mat = scipy.tile(self.params[-self.num_dim:], (tau.shape[0], 1))
          tau_over_l = tau / l_mat
          tau_over_l[(tau == 0) & (l_mat == 0)] = 0.0
          r2l2 = scipy.sum((tau_over_l)**2, axis=1)
          if return_l:
              return (r2l2, l_mat)
          else:
              return r2l2
docs: r"""Compute the anisotropic :math:`r^2/l^2` term for the given `tau`.
          
          Here, :math:`\tau=X_i-X_j` is the difference vector. Computes
          .. math::
              
              \frac{r^2}{l^2} = \sum_i\frac{\tau_i^2}{l_{i}^{2}}
          
          Assumes that the length parameters are the last `num_dim` elements of
          :py:attr:`self.params`.
          
          Where `l` and `tau` are both zero, that term is set to zero.
          
          Parameters
          ----------
          tau : :py:class:`Array`, (`M`, `D`)
              `M` inputs with dimension `D`.
          return_l : bool, optional
              Set to True to return a tuple of (`tau`, `l_mat`), where `l_mat`
              is the matrix of length scales to match the shape of `tau`. Default
              is False (only return `tau`).
          
          Returns
          -------
          r2l2 : :py:class:`Array`, (`M`,)
              Anisotropically scaled distances squared.
          l_mat : :py:class:`Array`, (`M`, `D`)
              The (`D`,) array of length scales repeated for each of the `M`
              inputs. Only returned if `return_l` is True.