code: def test_concatenated_predict_sarimax(use_exog, trend):
      endog = np.arange(100).reshape(100, 1) * 1.0
      exog = np.ones(100) if use_exog else None
      if use_exog:
          exog[10:30] = 2.0
  
      trend_params = [0.1]
      ar_params = [0.5]
      exog_params = [1.2]
      var_params = [1.0]
  
      params = []
      if trend in ["c", "t"]:
          params += trend_params
      params += ar_params
      if use_exog:
          params += exog_params
      params += var_params
  
      y1 = endog.copy()
      y1[-50:] = np.nan
      mod1 = sarimax.SARIMAX(
          y1, order=(1, 1, 0), trend=trend, exog=exog
      )
      res1 = mod1.smooth(params)
      p1 = res1.get_prediction()
      pr1 = p1.prediction_results
  
      x2 = exog[:50] if use_exog else None
      mod2 = sarimax.SARIMAX(
          endog[:50], order=(1, 1, 0), trend=trend, exog=x2
      )
      res2 = mod2.smooth(params)
      x2f = exog[50:] if use_exog else None
      p2 = res2.get_prediction(start=0, end=99, exog=x2f)
      pr2 = p2.prediction_results
  
      attrs = (
          pr1.representation_attributes
          + pr1.filter_attributes
          + pr1.smoother_attributes
      )
      for key in attrs:
          assert_allclose(getattr(pr2, key), getattr(pr1, key))
  
file_path: statsmodels/statsmodels/tsa/statespace/tests/test_prediction.py
imports: import pytest
  import numpy
  import pandas
  from numpy.testing import assert_equal, assert_raises, assert_allclose, assert_
  from statsmodels import datasets
  from statsmodels.tsa.statespace import sarimax, varmax
  from statsmodels.tsa.statespace.tests.test_impulse_responses import TVSS
label_desc: Use image node_id 3 for calling a global function with example usage: test_concatenated_predict_sarimax(use_exog, trend) without return types