code: def setup_model(
      request,
      austourists,
      oildata,
      ets_austourists_fit_results_R,
      ets_oildata_fit_results_R,
  ):
      params = request.param
      error, trend, seasonal, damped = params[0:4]
      data = params[4]
      if data == "austourists":
          data = austourists
          seasonal_periods = 4
          results = ets_austourists_fit_results_R[damped]
      else:
          data = oildata
          seasonal_periods = None
          results = ets_oildata_fit_results_R[damped]
  
      name = short_model_name(error, trend, seasonal)
      if name not in results:
          pytest.skip(
              f"model {name} not implemented or not converging in R"
          )
  
      results_R = results[name]
      params = get_params_from_R(results_R)
  
      model = ETSModel(
          data,
          seasonal_periods=seasonal_periods,
          error=error,
          trend=trend,
          seasonal=seasonal,
          damped_trend=damped,
      )
  
      return model, params, results_R
  
imports: from statsmodels.compat.pandas import QUARTER_END
  from statsmodels.compat.platform import PLATFORM_LINUX32, PLATFORM_WIN
  from itertools import product
  import json
  import pathlib
  import numpy
  from numpy.testing import assert_allclose, assert_almost_equal
  import pandas
  import pytest
  import scipy.stats
  from statsmodels.tsa.exponential_smoothing.ets import ETSModel
  import statsmodels.tsa.holtwinters
  import statsmodels.tsa.statespace.exponential_smoothing
label_desc: Use image node_id 3 for calling a global function with example usage: setup_model(request, austourists, oildata, ets_austourists_fit_results_R, ets_oildata_fit_results_R) and returns: model,  params,  results_R