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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,localizer=None):
 """calculate the approximate covariance matrix implied by the ensemble using mean-differencing operation at the core of EnKF Parameters localizer : pyemu.Matrix covariance localizer to apply Returns ------- cov : pyemu.Cov covariance matrix """





        mean = np.array(self.mean(axis=0))
        delta = self.as_pyemu_matrix(typ=Cov)
        for i in range(self.shape[0]):
            delta.x[i, :] -= mean
        delta *= (1.0 / np.sqrt(float(self.shape[0] - 1.0)))

        if localizer is not None:
            delta = delta.T * delta
            return delta.hadamard_product(localizer)

        return delta.T * delta