code: def test_get_prediction_memory_conserve():
      endog = dta["infl"].iloc[:20]
      mod1 = sarimax.SARIMAX(
          endog, order=(1, 0, 0), concentrate_scale=True
      )
      mod2 = sarimax.SARIMAX(
          endog, order=(1, 0, 0), concentrate_scale=True
      )
  
      mod1.ssm.set_conserve_memory(MEMORY_CONSERVE)
      assert_equal(mod1.ssm.conserve_memory, MEMORY_CONSERVE)
      assert_equal(mod2.ssm.conserve_memory, 0)
  
      res1 = mod1.filter([0])
      res2 = mod2.filter([0])
      assert_equal(res1.filter_results.conserve_memory, MEMORY_CONSERVE)
      assert_equal(
          res2.filter_results.conserve_memory, MEMORY_NO_SMOOTHING
      )
  
      p1 = res1.get_prediction()
      p2 = res2.get_prediction()
  
      assert_allclose(p1.predicted_mean, p2.predicted_mean)
      assert_allclose(p1.se_mean, np.nan)
      assert_allclose(p1.conf_int(), np.nan)
  
      s1 = p1.summary_frame()
      s2 = p2.summary_frame()
  
      assert_allclose(s1["mean"], s2["mean"])
      assert_allclose(s1.mean_se, np.nan)
      assert_allclose(s1.mean_ci_lower, np.nan)
      assert_allclose(s1.mean_ci_upper, np.nan)
  
file_path: statsmodels/statsmodels/tsa/statespace/tests/test_conserve_memory.py
imports: import numpy
  import pandas
  import pytest
  from numpy.testing import assert_equal, assert_allclose, assert_
  from statsmodels.datasets import macrodata
  from statsmodels.tsa.statespace import sarimax, varmax, dynamic_factor
  from statsmodels.tsa.statespace.kalman_filter import MEMORY_NO_FORECAST_MEAN, MEMORY_NO_FORECAST_COV, MEMORY_NO_PREDICTED_MEAN, MEMORY_NO_PREDICTED_COV, MEMORY_NO_PREDICTED, MEMORY_NO_SMOOTHING, MEMORY_NO_GAIN, MEMORY_CONSERVE
label_desc: Use image node_id 9 for calling a global function with example usage: test_get_prediction_memory_conserve() without return types