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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 evo_blanket(self, beta, alpha):
 """ Creates Markov blanket for the variance latent variables Parameters beta : np.array Contains untransformed starting values for latent variables alpha : np.array A vector of states Returns Markov blanket for variance latent variables """
                 

        # Markov blanket for each state
        evo_blanket = np.zeros(self.state_no)
        for i in range(evo_blanket.shape[0]):
            evo_blanket[i] = self.state_likelihood_markov_blanket(beta, alpha, i).sum()

        # If the family has additional parameters, add their markov blankets
        if self.family_z_no > 0:
            evo_blanket = np.append([self.likelihood_markov_blanket(beta).sum()]*(self.family_z_no),evo_blanket)

        return evo_blanket