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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 warped_gp_cubic_sine(max_iters=100):
 """ A test replicating the cubic sine regression problem from Snelson's paper. """

    X = (2 * np.pi) * np.random.random(151) - np.pi
    Y = np.sin(X) + np.random.normal(0,0.2,151)
    Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y])
    X = X[:, None]
    Y = Y[:, None]

    warp_k = GPy.kern.RBF(1)
    warp_f = GPy.util.warping_functions.TanhFunction(n_terms=2)
    warp_m = GPy.models.WarpedGP(X, Y, kernel=warp_k, warping_function=warp_f)
    warp_m['.*\.d'].constrain_fixed(1.0)
    m = GPy.models.GPRegression(X, Y)
    m.optimize_restarts(parallel=False, robust=True, num_restarts=5, max_iters=max_iters)
    warp_m.optimize_restarts(parallel=False, robust=True, num_restarts=5, max_iters=max_iters)
    #m.optimize(max_iters=max_iters)
    #warp_m.optimize(max_iters=max_iters)

    print(warp_m)
    print(warp_m['.*warp.*'])

    warp_m.predict_in_warped_space = False
    warp_m.plot(title="Warped GP - Latent space")
    warp_m.predict_in_warped_space = True
    warp_m.plot(title="Warped GP - Warped space")
    m.plot(title="Standard GP")
    warp_m.plot_warping()
    pb.show()