code: def test_query_marg(self):
      query_results = self.infer_alarm.query(variables=["HISTORY"])
      ve_results = self.alarm_ve.query(variables=["HISTORY"])
      self.assertTrue(query_results.__eq__(ve_results, atol=0.01))
  
      query_results = self.infer_alarm.query(
          variables=["HISTORY"], samples=self.samples
      )
      self.assertTrue(query_results.__eq__(ve_results, atol=0.01))
  
      query_results = self.infer_alarm.query(
          variables=["HISTORY", "CVP"], joint=True
      )
      ve_results = self.alarm_ve.query(
          variables=["HISTORY", "CVP"], joint=True
      )
      self.assertTrue(query_results.__eq__(ve_results, atol=0.01))
  
      query_results = self.infer_alarm.query(
          variables=["HISTORY", "CVP"], samples=self.samples, joint=True
      )
      self.assertTrue(query_results.__eq__(ve_results, atol=0.01))
  
      query_results = self.infer_alarm.query(
          variables=["HISTORY", "CVP"], joint=False
      )
      ve_results = self.alarm_ve.query(
          variables=["HISTORY", "CVP"], joint=False
      )
      for var in ["HISTORY", "CVP"]:
          self.assertTrue(
              query_results[var].__eq__(ve_results[var], atol=0.01)
          )
  
      query_results = self.infer_alarm.query(
          variables=["HISTORY", "CVP"],
          samples=self.samples,
          joint=False,
      )
      for var in ["HISTORY", "CVP"]:
          self.assertTrue(
              query_results[var].__eq__(ve_results[var], atol=0.01)
          )
  
imports: import unittest
  from pgmpy import config
  from pgmpy.factors.discrete import DiscreteFactor, TabularCPD
  from pgmpy.inference import ApproxInference, VariableElimination
  from pgmpy.models import DynamicBayesianNetwork
  from pgmpy.utils import get_example_model
label_desc: Use image node_id 2 for calling the TestApproxInferenceBNTorch obj's underlying member method code with example usage: obj.test_query_marg() without return types