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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 estimator_cov(self,method):
 """ Creates covariance matrix for the estimators Parameters method : str Estimation method Returns A Covariance Matrix """
         
        
        Y = np.array([reg[self.lags:] for reg in self.data])    
        Z = self._create_Z(Y)
        if method == 'OLS':
            sigma = self.ols_covariance()
        else:           
            sigma = self.custom_covariance(self.latent_variables.get_z_values())
        return np.kron(np.linalg.inv(np.dot(Z,np.transpose(Z))), sigma)