code: def sparse_toy_linear_1d_classification_uncertain_input(num_inducing=10, seed=default_seed, optimize=True, plot=True):
      
  
      try:import pods
      except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
      import numpy as np
      data = pods.datasets.toy_linear_1d_classification(seed=seed)
      Y = data['Y'][:, 0:1]
      Y[Y.flatten() == -1] = 0
      X = data['X']
      X_var = np.random.uniform(0.3,0.5,X.shape)
  
      # Model definition
      m = GPy.models.SparseGPClassificationUncertainInput(X, X_var, Y, num_inducing=num_inducing)
      m['.*len'] = 4.
  
      # Optimize
      if optimize:
          m.optimize()
  
      # 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: Sparse 1D classification example
  
      :param seed: seed value for data generation (default is 4).
      :type seed: int