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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 copy(self):
 """ Return a copy of the distribution. Returns ------- GaussianDistribution: copy of the distribution Examples -------- ['x1', 'x2', 'x3'] array([[ 1], [-3], [ 4]]) array([[ 4, 2, -2], [ 2, 5, -5], [-2, -5, 8]]) array([[ 0.3125 , -0.125 , 0. ], [-0.125 , 0.58333333, 0.33333333], [ 0. , 0.33333333, 0.33333333]]) """

        copy_distribution = GaussianDistribution(variables=self.variables,
                                                 mean=self.mean.copy(),
                                                 cov=self.covariance.copy())
        if self._precision_matrix is not None:
            copy_distribution._precision_matrix = self._precision_matrix.copy()

        return copy_distribution