code: def test_adjustment_query(self):
      for algo in ["ve", "bp"]:
          # Test adjustment with do operation.
          query1 = self.example_infer.query(
              variables=["Y"],
              do={"X": 1},
              adjustment_set={"Z"},
              inference_algo=algo,
          )
          np_test.assert_array_almost_equal(
              query1.values, np.array([0.7240, 0.2760])
          )
  
          query2 = self.example_infer.query(
              variables=["Y"],
              do={"X": 1},
              adjustment_set={"W"},
              inference_algo=algo,
          )
          np_test.assert_array_almost_equal(
              query2.values, np.array([0.7240, 0.2760])
          )
  
          # Test adjustment without do operation.
          query3 = self.example_infer.query(["Y"], adjustment_set=["W"])
          np_test.assert_array_almost_equal(
              query3.values, np.array([0.62, 0.38])
          )
  
          query4 = self.example_infer.query(["Y"], adjustment_set=["Z"])
          np_test.assert_array_almost_equal(
              query4.values, np.array([0.62, 0.38])
          )
  
          query5 = self.example_infer.query(
              ["Y"], adjustment_set=["W", "Z"]
          )
          np_test.assert_array_almost_equal(
              query5.values, np.array([0.62, 0.38])
          )
  
imports: import unittest
  import numpy
  import numpy.testing
  import pandas
  from pgmpy.factors.discrete import TabularCPD
  from pgmpy.inference.CausalInference import CausalInference
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 6 for calling the TestDoQuery obj's underlying member method code with example usage: obj.test_adjustment_query() without return types