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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 CAPM(self, benchmark, has_const=False, use_const=True):
 """Interface to OLS regression against `benchmark`. `self.alpha()`, `self.beta()` and several other methods stem from here. For the full method set, see `pyfinance.ols.OLS`. Parameters benchmark : {pd.Series, TSeries, pd.DataFrame, np.ndarray} The benchmark securitie(s) to which `self` is compared. has_const : bool, default False Specifies whether `benchmark` includes a user-supplied constant (a column vector). If False, it is added at instantiation. use_const : bool, default True Whether to include an intercept term in the model output. Note the difference between `has_const` and `use_const`: the former specifies whether a column vector of 1s is included in the input; the latter specifies whether the model itself should include a constant (intercept) term. Exogenous data that is ~N(0,1) would have a constant equal to zero; specify use_const=False in this situation. Returns ------- pyfinance.ols.OLS """


        return ols.OLS(
            y=self, x=benchmark, has_const=has_const, use_const=use_const
        )