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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_model(self):
 """ Returns an instance of Bayesian Model or Markov Model. Varibles are in the pattern var_0, var_1, var_2 where var_0 is 0th index variable, var_1 is 1st index variable. Return ------ model: an instance of Bayesian or Markov Model. Examples -------- """

        if self.network_type == 'BAYES':
            model = BayesianModel()
            model.add_nodes_from(self.variables)
            model.add_edges_from(self.edges)

            tabular_cpds = []
            for cpd in self.tables:
                child_var = cpd[0]
                states = int(self.domain[child_var])
                arr = list(map(float, cpd[1]))
                values = np.array(arr)
                values = values.reshape(states, values.size // states)
                tabular_cpds.append(TabularCPD(child_var, states, values))

            model.add_cpds(*tabular_cpds)
            return model

        elif self.network_type == 'MARKOV':
            model = MarkovModel(self.edges)

            factors = []
            for table in self.tables:
                variables = table[0]
                cardinality = [int(self.domain[var]) for var in variables]
                value = list(map(float, table[1]))
                factor = DiscreteFactor(variables=variables, cardinality=cardinality, values=value)
                factors.append(factor)

            model.add_factors(*factors)
            return model