code: def testGetLatentCov(self):
      # for forward filter
      self.dlm5._forwardFilter(start=0, end=99, renew=False)
      self.dlm5.result.filteredSteps = [0, 99]
      filteredTrend = self.dlm5._getLatentCov(
          filterType="forwardFilter", name="trend", start=0, end=99
      )
      diff = 0.0
      for i in range(len(filteredTrend)):
          diff += abs(
              filteredTrend[i][0, 0]
              - self.dlm5.result.filteredCov[i][0, 0]
          )
      self.assertAlmostEqual(diff, 0)
  
      # for prediction
      predictedTrend = self.dlm5._getLatentCov(
          filterType="predict", name="trend", start=0, end=99
      )
      diff = 0.0
      for i in range(len(predictedTrend)):
          diff += abs(
              predictedTrend[i][0, 0]
              - self.dlm5.result.predictedCov[i][0, 0]
          )
      self.assertAlmostEqual(diff, 0)
  
      # for backward smoother
      self.dlm5._backwardSmoother(start=99)
      self.dlm5.result.smoothedSteps = [0, 99]
      smoothedTrend = self.dlm5._getLatentCov(
          filterType="backwardSmoother", name="trend", start=0, end=99
      )
      diff = 0.0
      for i in range(len(smoothedTrend)):
          diff += abs(
              smoothedTrend[i][0, 0]
              - self.dlm5.result.smoothedCov[i][0, 0]
          )
      self.assertAlmostEqual(diff, 0)
  
file_path: pydlm/tests/access/test_dlmGet.py
imports: import unittest
  from pydlm.modeler.trends import trend
  from pydlm.modeler.autoReg import autoReg
  from pydlm.access._dlmGet import _dlmGet
label_desc: Use image node_id 3 for calling the test_dlmGet obj's underlying member method code with example usage: obj.testGetLatentCov() without return types