code: def test_stepwise_with_simple_differencing():
      def do_fit(simple_differencing):
          return pm.auto_arima(
              wineind,
              start_p=1,
              start_q=1,
              max_p=2,
              max_q=2,
              m=2,
              start_P=0,
              seasonal=True,
              d=1,
              D=1,
              stepwise=True,
              error_action="ignore",
              sarimax_kwargs={
                  "simple_differencing": simple_differencing
              },
              maxiter=2,
          )
  
      # show that we can forecast even after the
      # pickling (this was fit in parallel)
      seasonal_fit = do_fit(False)
      seasonal_fit.predict(n_periods=10)
  
      # ensure summary still works
      seasonal_fit.summary()
  
      # Show we can predict on seasonal where conf_int is true
      seasonal_fit.predict(n_periods=10, return_conf_int=True)
  
      # We should get the same order when simple_differencing
      simple = do_fit(True)
      assert simple.order == seasonal_fit.order
      assert simple.seasonal_order == seasonal_fit.seasonal_order
  
imports: import numpy
  import pandas
  import pmdarima
  from pmdarima.arima import auto
  from pmdarima.arima.utils import nsdiffs
  from pmdarima.warnings import ModelFitWarning
  from pmdarima.compat.pytest import pytest_error_str, pytest_warning_messages
  from numpy.testing import assert_allclose
  from numpy.testing import assert_array_almost_equal
  import os
  from os.path import abspath, dirname
  import pytest
label_desc: Use image node_id 20 for calling a global function with example usage: test_stepwise_with_simple_differencing() and returns: pm