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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 cond_gaussian(self, dims, v):
 """ Returns mean and variance of the conditional probability defined by a set of dimension and at a given vector. @param dims : set of dimension to which respect conditional probability is taken @param v : vector defining the position where the conditional probability is taken. v shape is defined by the size of the set of dims. """

        (d, c, b, a) = numpy.split_matrix(self.sigma, dims)
        (mu2, mu1) = numpy.split_vector(self.mu, dims)
        d_inv = numpy.linalg.inv(d)
        mu = mu1 + numpy.dot(numpy.dot(b, d_inv), v - mu2)
        sigma = a - numpy.dot(b, numpy.dot(d_inv, c))
        return Gaussian(mu, sigma)