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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 rwishart_cov(n, C):
 """ Return a Wishart random matrix. :Parameters: n : int Degrees of freedom, > 0. C : matrix Symmetric and positive definite """

    # return rwishart(n, np.linalg.inv(C))

    p = np.shape(C)[0]
    # Need cholesky decomposition of precision matrix C^-1?
    sig = np.linalg.cholesky(C)

    if n <= (p-1):
        raise ValueError('Wishart parameter n must be greater '
                         'than size of matrix.')

    norms = np.random.normal(size=(p * (p - 1)) // 2)
    chi_sqs = np.sqrt(np.random.chisquare(df=np.arange(n, n - p, -1)))
    A = flib.expand_triangular(chi_sqs, norms)

    flib.dtrmm_wrap(sig, A, side='L', uplo='L', transa='N', alpha=1.)
    w = np.asmatrix(np.dot(A, A.T))
    flib.symmetrize(w)
    return w