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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 _linear_predictor(self, X=None, modelmat=None, b=None, term=-1):
 """linear predictor compute the linear predictor portion of the model ie multiply the model matrix by the spline basis coefficients Parameters --------- at least 1 of (X, modelmat) and at least 1 of (b, feature) X : array-like of shape (n_samples, m_features) or None, optional containing the input dataset if None, will attempt to use modelmat modelmat : array-like or None, optional contains the spline basis for each feature evaluated at the input values for each feature, ie model matrix if None, will attempt to construct the model matrix from X b : array-like or None, optional contains the spline coefficients if None, will use current model coefficients feature : int, optional feature for which to compute the linear prediction if -1, will compute for all features Returns ------- lp : np.array of shape (n_samples,) """

        if modelmat is None:
            modelmat = self._modelmat(X, term=term)
        if b is None:
            b = self.coef_[self.terms.get_coef_indices(term)]
        return modelmat.dot(b).flatten()