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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_tables(self):
 """ Returns list of tuple of child variable and CPD in case of Bayesian and list of tuple of scope of variables and values in case of Markov. Returns ------- list : list of tuples of child variable and values in Bayesian list of tuples of scope of variables and values in case of Markov. Example ------- [(['var_0', 'var_1'], ['4.000', '2.400', '1.000', '0.000']), (['var_0', 'var_1', 'var_2'], ['2.2500', '3.2500', '3.7500', '0.0000', '0.0000', '10.0000', '1.8750', '4.0000', '3.3330', '2.0000', '2.0000', '3.4000'])] """

        tables = []
        for function in range(0, self.no_functions):
            function_variables = self.grammar.parseString(self.network)['fun_' + str(function)]
            if isinstance(function_variables, int):
                function_variables = [function_variables]
            if self.network_type == 'BAYES':
                child_var = "var_" + str(function_variables[-1])
                values = self.grammar.parseString(self.network)['fun_values_' + str(function)]
                tables.append((child_var, list(values)))
            elif self.network_type == "MARKOV":
                function_variables = ["var_" + str(var) for var in function_variables]
                values = self.grammar.parseString(self.network)['fun_values_' + str(function)]
                tables.append((function_variables, list(values)))
        return tables