code: def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=None, optimize=True, plot=True):
      
      try:import pods
      except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
      data = pods.datasets.crescent_data(seed=seed)
      Y = data['Y']
      Y[Y.flatten()==-1] = 0
  
      if model_type == 'Full':
          m = GPy.models.GPClassification(data['X'], Y, kernel=kernel)
  
      elif model_type == 'DTC':
          m = GPy.models.SparseGPClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)
          m['.*len'] = 10.
  
      elif model_type == 'FITC':
          m = GPy.models.FITCClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)
          m['.*len'] = 3.
      if optimize:
          m.optimize(messages=1)
  
      if plot:
          m.plot()
  
      print(m)
      return m
docs: Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
  
      :param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
      :param inducing: number of inducing variables (only used for 'FITC' or 'DTC').
      :type inducing: int
      :param seed: seed value for data generation.
      :type seed: int
      :param kernel: kernel to use in the model
      :type kernel: a GPy kernel