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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 _raw_predict(self, Xnew, full_cov=False, kern=None):
 """ For making predictions, does not account for normalization or likelihood full_cov is a boolean which defines whether the full covariance matrix of the prediction is computed. If full_cov is False (default), only the diagonal of the covariance is returned. .. math:: p(f*|X*, X, Y) = \int^{\inf}_{\inf} p(f*|f,X*)p(f|X,Y) df = MVN\left(\nu + N,f*| K_{x*x}(K_{xx})^{-1}Y, \frac{\nu + \beta - 2}{\nu + N - 2}K_{x*x*} - K_{xx*}(K_{xx})^{-1}K_{xx*}\right) \nu := \texttt{Degrees of freedom} """

        mu, var = self.posterior._raw_predict(kern=self.kern if kern is None else kern, Xnew=Xnew,
                                              pred_var=self._predictive_variable, full_cov=full_cov)
        if self.mean_function is not None:
            mu += self.mean_function.f(Xnew)
        return mu, var