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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 sparse_GP_regression_2D(num_samples=400, num_inducing=50, max_iters=100, optimize=True, plot=True, nan=False):
 """Run a 2D example of a sparse GP regression."""

    np.random.seed(1234)
    X = np.random.uniform(-3., 3., (num_samples, 2))
    Y = np.sin(X[:, 0:1]) * np.sin(X[:, 1:2]) + np.random.randn(num_samples, 1) * 0.05
    if nan:
        inan = np.random.binomial(1,.2,size=Y.shape)
        Y[inan] = np.nan

    # construct kernel
    rbf = GPy.kern.RBF(2)

    # create simple GP Model
    m = GPy.models.SparseGPRegression(X, Y, kernel=rbf, num_inducing=num_inducing)

    # contrain all parameters to be positive (but not inducing inputs)
    m['.*len'] = 2.

    m.checkgrad()

    # optimize
    if optimize:
        m.optimize('tnc', messages=1, max_iters=max_iters)

    # plot
    if plot:
        m.plot()

    print(m)
    return m