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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 corrgroups60(display=False):
 """ Correlated Groups 60 A simulated dataset with tight correlations among distinct groups of features. """


    # set a constant seed
    old_seed = np.random.seed()
    np.random.seed(0)

    # generate dataset with known correlation
    N = 1000
    M = 60

    # set one coefficent from each group of 3 to 1
    beta = np.zeros(M)
    beta[0:30:3] = 1

    # build a correlation matrix with groups of 3 tightly correlated features
    C = np.eye(M)
    for i in range(0,30,3):
        C[i,i+1] = C[i+1,i] = 0.99
        C[i,i+2] = C[i+2,i] = 0.99
        C[i+1,i+2] = C[i+2,i+1] = 0.99
    f = lambda X: np.matmul(X, beta)

    # Make sure the sample correlation is a perfect match
    X_start = np.random.randn(N, M)
    X_centered = X_start - X_start.mean(0)
    Sigma = np.matmul(X_centered.T, X_centered) / X_centered.shape[0]
    W = np.linalg.cholesky(np.linalg.inv(Sigma)).T
    X_white = np.matmul(X_centered, W.T)
    assert np.linalg.norm(np.corrcoef(np.matmul(X_centered, W.T).T) - np.eye(M)) < 1e-6 # ensure this decorrelates the data

    # create the final data
    X_final = np.matmul(X_white, np.linalg.cholesky(C).T)
    X = X_final
    y = f(X) + np.random.randn(N) * 1e-2

    # restore the previous numpy random seed
    np.random.seed(old_seed)

    return pd.DataFrame(X), y