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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 to_joint_gaussian(self):
 """ Return an equivalent Joint Gaussian Distribution. Examples -------- np.array([[5], [-1]]), 1) ['x1', 'x2'] array([[ 0.5 , 0.25 ], [ 0.25 , 0.375]]) array([[ 2.25 ], [ 0.875]]) """

        covariance = np.linalg.inv(self.K)
        mean = np.dot(covariance, self.h)

        return GaussianDistribution(self.variables, mean, covariance)