code: def test_hyperparameter_splits():
      """Tests the splits to use to determine the hyperparameters"""
  
      # 1.0 Recommended d, white noise, seasonal_period ----
      data = get_data("airline")
  
      FOLD = 1
      FH = 60
      TRAIN_SIZE = len(data) - FH
      # Train set
      data[:TRAIN_SIZE] = 1
      print("Experiment 1 ----")
      exp1 = TSForecastingExperiment()
      exp1.setup(data=data, fh=FH, fold=FOLD)
  
      print("Experiment 2 ----")
      exp2 = TSForecastingExperiment()
      exp2.setup(
          data=data, hyperparameter_split="train", fh=FH, fold=FOLD
      )
  
      assert exp1.primary_sp_to_use != exp2.primary_sp_to_use
      assert exp1.lowercase_d != exp2.lowercase_d
      assert exp1.white_noise != exp2.white_noise
      # uppercase_d turns out to be the same, hence tested separately
      assert exp1.uppercase_d == exp2.uppercase_d
  
      # 2.0 Recommended Seasonal D
      data = get_data("airline")
  
      FOLD = 1
      FH = 36
      TRAIN_SIZE = len(data) - FH
  
      np.random.seed(42)
      indices = np.random.randint(1, int(TRAIN_SIZE / 2), 12)
      data.iloc[indices] = 200
  
      exp1 = TSForecastingExperiment()
      exp1.setup(data=data, fh=FH, fold=FOLD)
  
      exp2 = TSForecastingExperiment()
      exp2.setup(
          data=data, hyperparameter_split="train", fh=FH, fold=FOLD
      )
  
      assert exp1.uppercase_d != exp2.uppercase_d
  
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 16 for calling a global function with example usage: test_hyperparameter_splits() without return types