code: def test_fit(self):
      model = DBN(
          [
              (("A", 0), ("B", 0)),
              (("A", 0), ("C", 0)),
              (("B", 0), ("D", 0)),
              (("C", 0), ("D", 0)),
              (("A", 0), ("A", 1)),
              (("B", 0), ("B", 1)),
              (("C", 0), ("C", 1)),
              (("D", 0), ("D", 1)),
          ]
      )
  
      data = np.random.randint(low=0, high=2, size=(10000, 20))
      colnames = []
      for t in range(5):
          colnames.extend([("A", t), ("B", t), ("C", t), ("D", t)])
      df = pd.DataFrame(data, columns=colnames)
      model.fit(df)
  
      self.assertTrue(model.check_model())
      self.assertEqual(len(model.cpds), 8)
      for cpd in model.cpds:
          np_test.assert_almost_equal(cpd.values, 0.5, decimal=1)
  
      self.assertRaises(ValueError, model.fit, df, "bayesian")
      self.assertRaises(ValueError, model.fit, df.values)
      wrong_colnames = []
      for t in range(5):
          wrong_colnames.extend(
              [("A", t + 1), ("B", t + 1), ("C", t + 1), ("D", t + 1)]
          )
      df.columns = wrong_colnames
      self.assertRaises(ValueError, model.fit, df)
  
imports: import unittest
  import numpy
  import numpy.testing
  import pandas
  import pgmpy.tests.help_functions
  from pgmpy.factors.discrete import TabularCPD
  from pgmpy.inference import CausalInference, DBNInference, VariableElimination
  from pgmpy.models import BayesianNetwork
  from pgmpy.models import DynamicBayesianNetwork
label_desc: Use image node_id 14 for calling the TestDynamicBayesianNetworkMethods obj's underlying member method code with example usage: obj.test_fit() without return types