How to use the dowhy.causal_estimators.instrumental_variable_estimator.InstrumentalVariableEstimator function in dowhy

                             [(0.4, InstrumentalVariableEstimator, [0,1], [1,2], [0,], [1, 2], [False, True], [False,]),])
    def test_average_treatment_effect(self, error_tolerance, Estimator,
            num_common_causes, num_instruments, num_effect_modifiers,
            num_treatments, treatment_is_binary, outcome_is_binary
            ):
        estimator_tester = TestEstimator(error_tolerance, Estimator)
        # Not using testsuite from .base/TestEstimtor, custom code below
        args_dict = {
                'num_common_causes': num_common_causes,
                'num_instruments': num_instruments,
                'num_effect_modifiers': num_effect_modifiers,
                'num_treatments': num_treatments,
                'treatment_is_binary': treatment_is_binary,
                'outcome_is_binary': outcome_is_binary
                }
        keys, values = zip(*args_dict.items())
        configs = [dict(zip(keys, v)) for v in itertools.product(*values)]

def _estimate_effect(self):
        upper_limit = self.rd_threshold_value + self.rd_bandwidth
        lower_limit = self.rd_threshold_value - self.rd_bandwidth
        rows_filter = np.s_[(self.rd_variable >= lower_limit) & (self.rd_variable <= upper_limit)]
        local_rd_variable = self.rd_variable[rows_filter]
        local_treatment_variable = self._treatment[self._treatment_name[0]][rows_filter] # indexing by treatment name again since this method assumes a single-dimensional treatment
        local_outcome_variable = self._outcome[rows_filter]
        local_df = pd.DataFrame(data={
            'local_rd_variable': local_rd_variable,
            'local_treatment': local_treatment_variable,
            'local_outcome': local_outcome_variable
        })
        print(local_df)
        iv_estimator = InstrumentalVariableEstimator(
            local_df,
            self._target_estimand,
            ['local_treatment'],
            ['local_outcome'],
            test_significance=self._significance_test,
            params={'iv_instrument_name': 'local_rd_variable'}
        )
        est = iv_estimator.estimate_effect()
        return est