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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 _covariance_matrix(self, type='noise'):
 """ Constructs the covariance matrix from PCA residuals """

        if type == 'sampling':
            return self.sigma**2/(self.n-1)
        elif type == 'noise':
            return 4*self.sigma*N.var(self.rotated(), axis=0)