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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 _K_compute_eq(self):
 """Compute covariance for latent covariance."""

        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)