code: def toy_rbf_1d(seed=default_seed, num_samples=500):
      
      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."}
docs: 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