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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 _score(self, Y, design, beta, scan_onsets, beta0, rho_e, sigma_e, rho_X0, sigma2_X0):
 """ Given the data Y, and the spatial pattern beta0 of nuisance time series, return the cross-validated score of the data Y given all parameters of the subject estimated during the first step. It is assumed that the user has design matrix built for the data Y. Both beta and beta0 are posterior expectation estimated from training data with the estimated covariance matrix U and SNR serving as prior. We marginalize X0 instead of fitting it in this function because this function is for the purpose of evaluating model no new data. We should avoid doing any additional fitting when performing cross-validation. The hypothetic response to the task will be subtracted, and the unknown nuisance activity which contributes to the data through beta0 will be marginalized. """

        logger.info('Estimating cross-validated score for new data.')
        n_T = Y.shape[0]
        if design is not None:
            Y = Y - np.dot(design, beta)
        # The function works for both full model and null model.
        # If design matrix is not provided, the whole data is
        # used as input for _forward_step. If design matrix is provided,
        # residual after subtracting design * beta is fed to _forward_step
        T_X = np.diag(rho_X0)
        Var_X = sigma2_X0 / (1 - rho_X0**2)
        Var_dX = sigma2_X0
        # Prior parmeters for X0: T_X is transitioning matrix, Var_X
        # is the marginal variance of the first time point. Var_dX is the
        # variance of the updating noise.
        sigma2_e = sigma_e ** 2
        # variance of voxel-specific updating noise component
        scan_onsets = np.setdiff1d(scan_onsets, n_T).astype(int)
        n_scan = scan_onsets.size
        total_log_p = 0
        for scan, onset in enumerate(scan_onsets):
            # Forward step
            if scan == n_scan - 1:
                offset = n_T
            else:
                offset = scan_onsets[scan + 1]
            _, _, _, log_p_data, _, _, _, _, _ = \
                self._forward_step(
                    Y[onset:offset, :], T_X, Var_X, Var_dX, rho_e, sigma2_e,
                    beta0)
            total_log_p += log_p_data
        return total_log_p