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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 beta_adj(self, benchmark, adj_factor=2 / 3, **kwargs):
 """Adjusted beta. Beta that is adjusted to reflect the tendency of beta to be mean reverting. [Source: CFA Institute] Formula: adj_factor * raw_beta + (1 - adj_factor) Parameters benchmark : {pd.Series, TSeries, pd.DataFrame, np.ndarray} The benchmark securitie(s) to which `self` is compared. Returns ------- float or np.ndarray If `benchmark` is 1d, returns a scalar. If `benchmark` is 2d, returns a 1d ndarray. Reference --------- .. _Blume, Marshall. "Betas and Their Regression Tendencies." http://www.stat.ucla.edu/~nchristo/statistics417/blume_betas.pdf """


        beta = self.beta(benchmark=benchmark, **kwargs)
        return adj_factor * beta + (1 - adj_factor)