code: def test_Sarimax_consolidate_kwargs_fit_kwargs(kwargs, _fit_kwargs):
      """
      Test if `sarimax._fit_kwargs` are correctly consolidate.
      """
  
      sarimax = Sarimax(**kwargs)
      results = (sarimax._fit_kwargs, sarimax.sm_fit_kwargs)
  
      # First expected _fit_kwargs, second sm_fit_kwargs
      expected = (
          {
              "method": "lbfgs",
              "maxiter": 50,
              "start_params": None,
              "disp": False,
          },
          kwargs["sm_fit_kwargs"],
      )
      expected[0].update(_fit_kwargs)
  
      assert all(isinstance(x, dict) for x in results)
      assert results[0] == expected[0]
      assert results[1] == expected[1]
  
imports: import re
  import pytest
  import numpy
  import pandas
  from sklearn.exceptions import NotFittedError
  from skforecast.Sarimax import Sarimax
  from statsmodels.tsa.statespace.sarimax import SARIMAX
  from statsmodels.tsa.statespace.sarimax import SARIMAXResults
  from statsmodels.tsa.statespace.sarimax import SARIMAXResultsWrapper
  from .fixtures_Sarimax import y
  from .fixtures_Sarimax import y_lw
  from .fixtures_Sarimax import exog
  from .fixtures_Sarimax import exog_lw
  from .fixtures_Sarimax import exog_predict
  from .fixtures_Sarimax import exog_lw_predict
  from .fixtures_Sarimax import y_datetime
  from .fixtures_Sarimax import exog_datetime
  from .fixtures_Sarimax import exog_predict_datetime
label_desc: Use image node_id 5 for calling a global function with example usage: test_Sarimax_consolidate_kwargs_fit_kwargs(kwargs, _fit_kwargs) without return types