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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 toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=True):
 """ Simple 1D classification example using Laplace approximation :param seed: seed value for data generation (default is 4). :type seed: int """


    try:import pods
    except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
    data = pods.datasets.toy_linear_1d_classification(seed=seed)
    Y = data['Y'][:, 0:1]
    Y[Y.flatten() == -1] = 0

    likelihood = GPy.likelihoods.Bernoulli()
    laplace_inf = GPy.inference.latent_function_inference.Laplace()
    kernel = GPy.kern.RBF(1)

    # Model definition
    m = GPy.core.GP(data['X'], Y, kernel=kernel, likelihood=likelihood, inference_method=laplace_inf)

    # Optimize
    if optimize:
        try:
            m.optimize('scg', messages=1)
        except Exception as e:
            return m

    # Plot
    if plot:
        from matplotlib import pyplot as plt
        fig, axes = plt.subplots(2, 1)
        m.plot_f(ax=axes[0])
        m.plot(ax=axes[1])

    print(m)
    return m