code: def test_seasonality_type_with_season_not_stricly_positive(
      index: str, seasonality_type: str, y: pd.Series
  ):
      """Tests the detection of the seasonality type with user defined type and
      data that has seasonality and is not strictly positive.
  
      Parameters
      ----------
      index : str
          Type of index. Options are: "RangeIndex" and "DatetimeIndex"
      seasonality_type : str
          The seasonality type to pass to setup
      y : pd.Series
          Dataset to use
      """
      # Make data not strictly positive
      y = y - y.max()
  
      # RangeIndex is default index
      if index == "DatetimeIndex":
          dates = pd.date_range(
              start="2020-01-01", periods=len(y), freq="MS"
          )
          y.index = dates
  
      err_msg = "Expected 'additive' seasonality, got something else"
      exp = TSForecastingExperiment()
      exp.setup(
          data=y, seasonality_type=seasonality_type, session_id=42
      )
      assert exp.seasonality_type == "add", err_msg
  
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 18 for calling a global function with example usage: test_seasonality_type_with_season_not_stricly_positive(index, seasonality_type, y) without return types