code: def d2logpdf_dlink2_dvar(self, link_f, y, Y_metadata=None):
          
          s_4 = 1.0/(self.variance**2)
          N = y.shape[0]
          D = link_f.shape[1]
          d2logpdf_dlink2_dvar = np.ones((N, D))*s_4
          return d2logpdf_dlink2_dvar
docs: Gradient of the hessian (d2logpdf_dlink2) w.r.t variance parameter (noise_variance)
  
          .. math::
              \\frac{d}{d\\sigma^{2}}(\\frac{d^{2} \\ln p(y_{i}|\\lambda(f_{i}))}{d^{2}\\lambda(f)}) = \\frac{1}{\\sigma^{4}}
  
          :param link_f: latent variables link(f)
          :type link_f: Nx1 array
          :param y: data
          :type y: Nx1 array
          :param Y_metadata: Y_metadata not used in gaussian
          :returns: derivative of log hessian evaluated at points link(f_i) and link(f_j) w.r.t variance parameter
          :rtype: Nx1 array