code: def test_enforce_pi(load_pos_and_neg_data):
      """Tests the enforcement of prediction interval"""
      data = load_pos_and_neg_data
  
      # With enforcement ----
      exp1 = TSForecastingExperiment()
      exp1.setup(data=data, point_alpha=0.5)
      num_models1 = len(exp1.models())
  
      # Without enforcement ----
      exp2 = TSForecastingExperiment()
      exp2.setup(data=data, point_alpha=None)
      num_models2 = len(exp2.models())
  
      # We know that some models do not offer PI capability, so the following
      # check is valid for now.
      assert num_models1 < num_models2
  
imports: import math
  import numpy
  import pandas
  import pytest
  from time_series_test_utils import _get_seasonal_values, _get_seasonal_values_alphanumeric, _return_data_seasonal_types_strictly_pos, _return_setup_args_raises, _return_splitter_args
  from pycaret.datasets import get_data
  from pycaret.time_series import TSForecastingExperiment
label_desc: Use image node_id 4 for calling a global function with example usage: test_enforce_pi(load_pos_and_neg_data) without return types