docs: def get_model(self):
          """
          Returns the model instance of the ProbModel.
  
          Return
          ---------------
          model: an instance of BayesianModel.
  
          Examples
          -------
          >>> reader = ProbModelXMLReader()
          >>> reader.get_model()
          """
          if self.probnet.get('type') == "BayesianNetwork":
              model = BayesianModel()
              model.add_nodes_from(self.probnet['Variables'].keys())
              model.add_edges_from(self.probnet['edges'].keys())
  
              tabular_cpds = []
              cpds = self.probnet['Potentials']
              for cpd in cpds:
                  var = list(cpd['Variables'].keys())[0]
                  states = self.probnet['Variables'][var]['States']
                  evidence = cpd['Variables'][var]
                  evidence_card = [len(self.probnet['Variables'][evidence_var]['States'])
                                   for evidence_var in evidence]
                  arr = list(map(float, cpd['Values'].split()))
                  values = np.array(arr)
                  values = values.reshape((len(states), values.size//len(states)))
                  tabular_cpds.append(TabularCPD(var, len(states), values, evidence, evidence_card))
  
              model.add_cpds(*tabular_cpds)
  
              variables = model.nodes()
              for var in variables:
                  for prop_name, prop_value in self.probnet['Variables'][var].items():
                      model.node[var][prop_name] = prop_value
              edges = model.edges()
  
              if nx.__version__.startswith('1'):
                  for edge in edges:
                      for prop_name, prop_value in self.probnet['edges'][edge].items():
                          model.edge[edge[0]][edge[1]][prop_name] = prop_value
              else:
                  for edge in edges:
                      for prop_name, prop_value in self.probnet['edges'][edge].items():
                          model.adj[edge[0]][edge[1]][prop_name] = prop_value
              return model
          else:
              raise ValueError("Please specify only Bayesian Network.")