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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 epomeo_gpx(max_iters=200, optimize=True, plot=True):
 """ Perform Gaussian process regression on the latitude and longitude data from the Mount Epomeo runs. Requires gpxpy to be installed on your system to load in the data. """

    try:import pods
    except ImportError:
        print('pods unavailable, see https://github.com/sods/ods for example datasets')
        return
    data = pods.datasets.epomeo_gpx()
    num_data_list = []
    for Xpart in data['X']:
        num_data_list.append(Xpart.shape[0])

    num_data_array = np.array(num_data_list)
    num_data = num_data_array.sum()
    Y = np.zeros((num_data, 2))
    t = np.zeros((num_data, 2))
    start = 0
    for Xpart, index in zip(data['X'], range(len(data['X']))):
        end = start+Xpart.shape[0]
        t[start:end, :] = np.hstack((Xpart[:, 0:1],
                                    index*np.ones((Xpart.shape[0], 1))))
        Y[start:end, :] = Xpart[:, 1:3]

    num_inducing = 200
    Z = np.hstack((np.linspace(t[:,0].min(), t[:, 0].max(), num_inducing)[:, None],
                   np.random.randint(0, 4, num_inducing)[:, None]))

    k1 = GPy.kern.RBF(1)
    k2 = GPy.kern.Coregionalize(output_dim=5, rank=5)
    k = k1**k2

    m = GPy.models.SparseGPRegression(t, Y, kernel=k, Z=Z, normalize_Y=True)
    m.constrain_fixed('.*variance', 1.)
    m.inducing_inputs.constrain_fixed()
    m.Gaussian_noise.variance.constrain_bounded(1e-3, 1e-1)
    m.optimize(max_iters=max_iters,messages=True)

    return m