code: def _K_compute_eq(self):
          
          t_eq = self._t[self._index==0]
          if self._t2 is None:
              if t_eq.size==0:
                  self._K_eq = np.zeros((0, 0))
                  return
              self._dist2 = np.square(t_eq[:, None] - t_eq[None, :])
          else:
              t2_eq = self._t2[self._index2==0]
              if t_eq.size==0 or t2_eq.size==0:
                  self._K_eq = np.zeros((t_eq.size, t2_eq.size))
                  return
              self._dist2 = np.square(t_eq[:, None] - t2_eq[None, :])
          
          self._K_eq = np.exp(-self._dist2/(2*self.lengthscale*self.lengthscale))
          if self.is_normalized:
              self._K_eq/=(np.sqrt(2*np.pi)*self.lengthscale)
docs: Compute covariance for latent covariance.