causalml/inference/iv/drivlearner.py [367:389]:
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        if p is None:
            p = (self.propensity_0, self.propensity_1)
        else:
            check_p_conditions(p[0], self.t_groups)
            check_p_conditions(p[1], self.t_groups)

        if isinstance(p[0], (np.ndarray, pd.Series)):
            treatment_name = self.t_groups[0]
            p = (
                {treatment_name: convert_pd_to_np(p[0])},
                {treatment_name: convert_pd_to_np(p[1])},
            )
        elif isinstance(p[0], dict):
            p = (
                {
                    treatment_name: convert_pd_to_np(_p)
                    for treatment_name, _p in p[0].items()
                },
                {
                    treatment_name: convert_pd_to_np(_p)
                    for treatment_name, _p in p[1].items()
                },
            )
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causalml/inference/iv/drivlearner.py [480:502]:
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        if p is None:
            p = (self.propensity_0, self.propensity_1)
        else:
            check_p_conditions(p[0], self.t_groups)
            check_p_conditions(p[1], self.t_groups)

        if isinstance(p[0], (np.ndarray, pd.Series)):
            treatment_name = self.t_groups[0]
            p = (
                {treatment_name: convert_pd_to_np(p[0])},
                {treatment_name: convert_pd_to_np(p[1])},
            )
        elif isinstance(p[0], dict):
            p = (
                {
                    treatment_name: convert_pd_to_np(_p)
                    for treatment_name, _p in p[0].items()
                },
                {
                    treatment_name: convert_pd_to_np(_p)
                    for treatment_name, _p in p[1].items()
                },
            )
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