class_docstr: Notes
  -----
  Cannot test against KFAS because they have a different behavior for
  missing entries. When an entry is missing, KFAS does not draw a simulation
  smoothed value for that entry, whereas we draw from the unconditional
  distribution. It appears there is nothing to definitively recommend one
  approach over the other, but it makes it difficult to line up the variates
  correctly in order to replicate results.
class_docstr_tok: ["Notes","--","--","-","Can","not","test","against","KFAS","because","they","have","a","different","behavior","for","missing","entries",".","When","an","entry","is","missing",",","KFAS","does","not","draw","a","simulation","smoothed","value","for","that","entry",",","whereas","we","draw","from","the","unconditional","distribution",".","It","appears","there","is","nothing","to","definitively","recommend","one","approach","over","the","other",",","but","it","makes","it","difficult","to","line","up","the","variates","correctly","in","order","to","replicate","results","."]
code: def setup_class(cls, *args, **kwargs):
      super().setup_class(missing="all", test_against_KFAS=False)
      cls.true_llf = 1305.739288
  
imports: import os
  import numpy
  import pandas
  from numpy.testing import assert_allclose, assert_equal
  import pytest
  from statsmodels import datasets
  from statsmodels.tsa.statespace import mlemodel, sarimax, structural, varmax
  from statsmodels.tsa.statespace.simulation_smoother import SIMULATION_STATE, SIMULATION_DISTURBANCE, SIMULATION_ALL
label_desc: Use image node_id 1 for calling the TestMultivariateVARKnownMissingAll obj's underlying member method code with example usage: obj.setup_class(cls) without return types