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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_rbf_1d(seed=default_seed, num_samples=500):
 """ Samples values of a function from an RBF covariance with very small noise for inputs uniformly distributed between -1 and 1. :param seed: seed to use for random sampling. :type seed: int :param num_samples: number of samples to sample in the function (default 500). :type num_samples: int """

    np.random.seed(seed=seed)
    num_in = 1
    X = np.random.uniform(low= -1.0, high=1.0, size=(num_samples, num_in))
    X.sort(axis=0)
    rbf = GPy.kern.RBF(num_in, variance=1., lengthscale=np.array((0.25,)))
    white = GPy.kern.White(num_in, variance=1e-2)
    kernel = rbf + white
    K = kernel.K(X)
    y = np.reshape(np.random.multivariate_normal(np.zeros(num_samples), K), (num_samples, 1))
    return {'X':X, 'Y':y, 'info': "Sampled " + str(num_samples) + " values of a function from an RBF covariance with very small noise for inputs uniformly distributed between -1 and 1."}