code: def test_memory_no_predicted():
      # Tests for forecasts with memory_no_predicted is set
      endog = [0.5, 1.2, 0.4, 0.6]
  
      mod = sarimax.SARIMAX(endog, order=(1, 0, 0))
      res1 = mod.filter([0.5, 1.0])
      mod.ssm.memory_no_predicted = True
      res2 = mod.filter([0.5, 1.0])
  
      # Make sure we really didn't store all of the values in res2
      assert_equal(res1.predicted_state.shape, (1, 5))
      assert_(res2.predicted_state is None)
      assert_equal(res1.predicted_state_cov.shape, (1, 1, 5))
      assert_(res2.predicted_state_cov is None)
  
      # Check that we can't do dynamic in-sample prediction
      assert_raises(ValueError, res2.predict, dynamic=True)
      assert_raises(ValueError, res2.get_prediction, dynamic=True)
  
      # Make sure the point forecasts are the same
      assert_allclose(res1.forecast(10), res2.forecast(10))
  
      # Make sure the confidence intervals are the same
      fcast1 = res1.get_forecast(10)
      fcast2 = res1.get_forecast(10)
  
      assert_allclose(fcast1.summary_frame(), fcast2.summary_frame())
  
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 2 for calling a global function with example usage: test_memory_no_predicted() without return types