code: def _initialize(self):
          r
          if self._mean is None or self._cov is None:
              return
  
          Q = self._cov["QS"][0][0]
          S = self._cov["QS"][1]
  
          if S.size > 0:
              self.tau[:] = 1 / npsum((Q * sqrt(S)) ** 2, axis=1)
          else:
              self.tau[:] = 0.0
          self.eta[:] = self._mean
          self.eta[:] *= self.tau
docs: r"""Initialize the mean and covariance of the posterior.
  
          Given that :math:`\tilde{\mathrm T}` is a matrix of zeros right before
          the first EP iteration, we have
  
          .. math::
  
              \boldsymbol\mu = \mathrm K^{-1} \mathbf m ~\text{ and }~
              \Sigma = \mathrm K
  
          as the initial posterior mean and covariance.