code: def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
      
      try:import pods
      except ImportError:raise ImportWarning('Need pods for example datasets. See https://github.com/sods/ods, or pip install pods.')
      data = pods.datasets.toy_linear_1d_classification(seed=seed)
      Y = data['Y'][:, 0:1]
      Y[Y.flatten() == -1] = 0
  
      # Model definition
      m = GPy.models.GPClassification(data['X'], Y)
  
      # Optimize
      if optimize:
          #m.update_likelihood_approximation()
          # Parameters optimization:
          m.optimize()
          #m.update_likelihood_approximation()
          #m.pseudo_EM()
  
      # 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
docs: Simple 1D classification example using EP approximation
  
      :param seed: seed value for data generation (default is 4).
      :type seed: int