code: def test_nonoccurring_values(self):
      mle = MaximumLikelihoodEstimator(
          self.m1,
          self.d1,
          state_names={
              "A": [0, 1, 23],
              "B": [0, 1],
              "C": [0, 42, 1],
              1: [2],
          },
      )
      cpds = [
          TabularCPD(
              "A",
              3,
              [[2.0 / 3], [1.0 / 3], [0]],
              state_names={"A": [0, 1, 23]},
          ),
          TabularCPD(
              "B", 2, [[2.0 / 3], [1.0 / 3]], state_names={"B": [0, 1]}
          ),
          TabularCPD(
              "C",
              3,
              [
                  [0.0, 0.0, 1.0, 1.0 / 3, 1.0 / 3, 1.0 / 3],
                  [0.0, 0.0, 0.0, 1.0 / 3, 1.0 / 3, 1.0 / 3],
                  [1.0, 1.0, 0.0, 1.0 / 3, 1.0 / 3, 1.0 / 3],
              ],
              evidence=["A", "B"],
              evidence_card=[3, 2],
              state_names={
                  "A": [0, 1, 23],
                  "B": [0, 1],
                  "C": [0, 42, 1],
              },
          ),
      ]
      self.assertEqual(mle.estimate_cpd("A"), cpds[0])
      self.assertEqual(mle.estimate_cpd("B"), cpds[1])
      self.assertEqual(mle.estimate_cpd("C"), cpds[2])
      self.assertEqual(len(mle.get_parameters(n_jobs=1)), 3)
  
imports: import unittest
  import numpy
  import pandas
  from joblib.externals.loky import get_reusable_executor
  from pgmpy import config
  from pgmpy.estimators import MaximumLikelihoodEstimator
  from pgmpy.factors.discrete import TabularCPD
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 8 for calling the TestMLETorch obj's underlying member method code with example usage: obj.test_nonoccurring_values() without return types