code: def test_add_cpds(self):
      self.model.add_cpds(self.cpd1)
      cpd = self.model.get_cpds("x1")
      self.assertEqual(cpd.variable, self.cpd1.variable)
      self.assertEqual(cpd.variance, self.cpd1.variance)
      self.assertEqual(cpd.beta_0, self.cpd1.beta_0)
  
      self.model.add_cpds(self.cpd2)
      cpd = self.model.get_cpds("x2")
      self.assertEqual(cpd.variable, self.cpd2.variable)
      self.assertEqual(cpd.variance, self.cpd2.variance)
      self.assertEqual(cpd.beta_0, self.cpd2.beta_0)
      self.assertEqual(cpd.evidence, self.cpd2.evidence)
      np_test.assert_array_equal(cpd.beta_vector, self.cpd2.beta_vector)
  
      self.model.add_cpds(self.cpd3)
      cpd = self.model.get_cpds("x3")
      self.assertEqual(cpd.variable, self.cpd3.variable)
      self.assertEqual(cpd.variance, self.cpd3.variance)
      self.assertEqual(cpd.beta_0, self.cpd3.beta_0)
      self.assertEqual(cpd.evidence, self.cpd3.evidence)
      np_test.assert_array_equal(cpd.beta_vector, self.cpd3.beta_vector)
  
      tab_cpd = TabularCPD(
          "grade",
          3,
          [
              [0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
              [0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
              [0.8, 0.8, 0.8, 0.8, 0.8, 0.8],
          ],
          evidence=["diff", "intel"],
          evidence_card=[2, 3],
      )
      self.assertRaises(ValueError, self.model.add_cpds, tab_cpd)
      self.assertRaises(ValueError, self.model.add_cpds, 1)
      self.assertRaises(ValueError, self.model.add_cpds, 1, tab_cpd)
  
file_path: pgmpy/pgmpy/tests/test_models/test_LinearGaussianBayesianNetwork.py
imports: import unittest
  import numpy
  import numpy.testing
  from pgmpy.factors.continuous import LinearGaussianCPD
  from pgmpy.factors.discrete import TabularCPD
  from pgmpy.models import LinearGaussianBayesianNetwork
label_desc: Use image node_id 3 for calling the TestLGBNMethods obj's underlying member method code with example usage: obj.test_add_cpds() without return types