code: def test_estimate_dag(self):
      for variant in ["orig", "stable", "parallel"]:
          ind = Independencies(["B", "C"], ["A", ["B", "C"], "D"])
          ind = ind.closure()
          estimator = PC(independencies=ind)
          model = estimator.estimate(
              variant="orig",
              ci_test="independence_match",
              return_type="dag",
              n_jobs=2,
              show_progress=False,
          )
          expected_edges = {("B", "D"), ("A", "D"), ("C", "D")}
          self.assertEqual(model.edges(), expected_edges)
  
          model = BayesianNetwork(
              [("A", "C"), ("B", "C"), ("B", "D"), ("C", "E")]
          )
          estimator = PC(independencies=model.get_independencies())
          estimated_model = estimator.estimate(
              variant="orig",
              ci_test="independence_match",
              return_type="dag",
              n_jobs=2,
              show_progress=False,
          )
          expected_edges_1 = set(model.edges())
          expected_edges_2 = {
              ("B", "C"),
              ("A", "C"),
              ("C", "E"),
              ("D", "B"),
          }
          self.assertTrue(
              (set(estimated_model.edges()) == expected_edges_1)
              or (set(estimated_model.edges()) == expected_edges_2)
          )
  
imports: import unittest
  import networkx
  import numpy
  import pandas
  from joblib.externals.loky import get_reusable_executor
  from pgmpy.estimators import PC
  from pgmpy.independencies import Independencies
  from pgmpy.models import BayesianNetwork
  from pgmpy.sampling import BayesianModelSampling
  from pgmpy.utils import get_example_model
label_desc: Use image node_id 3 for calling the TestPCEstimatorFromIndependencies obj's underlying member method code with example usage: obj.test_estimate_dag() without return types