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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_ode_eq(self, transpose=False):
 """Compute the cross covariances between latent exponentiated quadratic and observed ordinary differential equations. :param transpose: if set to false the exponentiated quadratic is on the rows of the matrix and is computed according to self._t, if set to true it is on the columns and is computed according to self._t2 (default=False). :type transpose: bool"""


        if self._t2 is not None:
            if transpose:
                t_eq = self._t[self._index==0]
                t_ode = self._t2[self._index2>0]
                index_ode = self._index2[self._index2>0]-1
            else:
                t_eq = self._t2[self._index2==0]
                t_ode = self._t[self._index>0]
                index_ode = self._index[self._index>0]-1
        else:
            t_eq = self._t[self._index==0]
            t_ode = self._t[self._index>0]
            index_ode = self._index[self._index>0]-1

        if t_ode.size==0 or t_eq.size==0:
            if transpose:
                self._K_eq_ode = np.zeros((t_eq.shape[0], t_ode.shape[0]))
            else:
                self._K_ode_eq = np.zeros((t_ode.shape[0], t_eq.shape[0]))
            return

        t_ode_mat = t_ode[:, None]
        t_eq_mat = t_eq[None, :]
        if self.delay is not None:
            t_ode_mat -= self.delay[index_ode, None]
        diff_t = (t_ode_mat - t_eq_mat)

        inv_sigma_diff_t = 1./self.sigma*diff_t
        decay_vals = self.decay[index_ode][:, None]
        half_sigma_d_i = 0.5*self.sigma*decay_vals

        if self.is_stationary:
            ln_part, signs = ln_diff_erfs(inf, half_sigma_d_i - inv_sigma_diff_t, return_sign=True)
        else:
            ln_part, signs = ln_diff_erfs(half_sigma_d_i + t_eq_mat/self.sigma, half_sigma_d_i - inv_sigma_diff_t, return_sign=True)
        sK = signs*np.exp(half_sigma_d_i*half_sigma_d_i - decay_vals*diff_t + ln_part)

        sK *= 0.5

        if not self.is_normalized:
            sK *= np.sqrt(np.pi)*self.sigma


        if transpose:
            self._K_eq_ode = sK.T
        else:
            self._K_ode_eq = sK