code: def _return_model_parameters():
      """Parameterize individual models.
      Returns the model names and the corresponding forecast horizons.
      Horizons are alternately picked to be either
          (1) integers or
          (2) numpy arrays (continuous)
          (3) numpy arrays (with gaps)
      """
      model_names = _return_model_names()
  
      parameters = []
      for i, name in enumerate(model_names):
          if i % 3 == 0:
              # Integer
              fh = random.randint(6, 24)
          elif i % 3 == 1:
              # numpy arrays (continuous)
              fh = np.arange(1, random.randint(13, 25))
          else:
              # i%3 = 2
              # numpy arrays (with gaps)
              fh = np.arange(
                  random.randint(6, 12), random.randint(13, 25)
              )
  
          parameters.append((name, fh))
  
      return parameters
  
imports: import random
  import numpy
  import pandas
  from pandas.testing import assert_frame_equal
  from sktime.forecasting.base import ForecastingHorizon
  from pycaret.containers.models.time_series import get_all_model_containers
  from pycaret.datasets import get_data
  from pycaret.time_series import TSForecastingExperiment
  from pycaret.utils.time_series import SeasonalPeriod
label_desc: Use image node_id 10 for calling a global function with example usage: _return_model_parameters() and returns: parameters