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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 ols_covariance(self):
 """ Creates OLS estimate of the covariance matrix Returns The OLS estimate of the covariance matrix """
         

        Y = np.array([reg[self.lags:reg.shape[0]] for reg in self.data])        
        return (1.0/(Y[0].shape[0]))*np.dot(self.residuals(Y),np.transpose(self.residuals(Y)))