econml/iv/dml/_dml.py [1043:1093]:
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        super().__init__(discrete_treatment=discrete_treatment,
                         discrete_instrument=discrete_instrument,
                         categories=categories,
                         cv=cv,
                         mc_iters=mc_iters,
                         mc_agg=mc_agg,
                         random_state=random_state)

    def _gen_featurizer(self):
        return clone(self.featurizer, safe=False)

    def _gen_model_y_xw(self):
        if self.model_y_xw == 'auto':
            model_y_xw = WeightedLassoCVWrapper(random_state=self.random_state)
        else:
            model_y_xw = clone(self.model_y_xw, safe=False)
        return _FirstStageWrapper(model_y_xw, True, self._gen_featurizer(),
                                  False, False)

    def _gen_model_t_xw(self):
        if self.model_t_xw == 'auto':
            if self.discrete_treatment:
                model_t_xw = LogisticRegressionCV(cv=WeightedStratifiedKFold(random_state=self.random_state),
                                                  random_state=self.random_state)
            else:
                model_t_xw = WeightedLassoCVWrapper(random_state=self.random_state)
        else:
            model_t_xw = clone(self.model_t_xw, safe=False)
        return _FirstStageWrapper(model_t_xw, False, self._gen_featurizer(),
                                  False, self.discrete_treatment)

    def _gen_model_t_xwz(self):
        if self.model_t_xwz == 'auto':
            if self.discrete_treatment:
                model_t_xwz = LogisticRegressionCV(cv=WeightedStratifiedKFold(random_state=self.random_state),
                                                   random_state=self.random_state)
            else:
                model_t_xwz = WeightedLassoCVWrapper(random_state=self.random_state)
        else:
            model_t_xwz = clone(self.model_t_xwz, safe=False)
        return _FirstStageWrapper(model_t_xwz, False, self._gen_featurizer(),
                                  False, self.discrete_treatment)

    def _gen_model_final(self):
        return clone(self.model_final, safe=False)

    def _gen_ortho_learner_model_nuisance(self):
        return _BaseDMLIVModelNuisance(self._gen_model_y_xw(), self._gen_model_t_xw(), self._gen_model_t_xwz())

    def _gen_ortho_learner_model_final(self):
        return _BaseDMLIVModelFinal(_FinalWrapper(self._gen_model_final(),
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econml/iv/dml/_dml.py [1362:1412]:
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        super().__init__(discrete_treatment=discrete_treatment,
                         discrete_instrument=discrete_instrument,
                         categories=categories,
                         cv=cv,
                         mc_iters=mc_iters,
                         mc_agg=mc_agg,
                         random_state=random_state)

    def _gen_featurizer(self):
        return clone(self.featurizer, safe=False)

    def _gen_model_y_xw(self):
        if self.model_y_xw == 'auto':
            model_y_xw = WeightedLassoCVWrapper(random_state=self.random_state)
        else:
            model_y_xw = clone(self.model_y_xw, safe=False)
        return _FirstStageWrapper(model_y_xw, True, self._gen_featurizer(),
                                  False, False)

    def _gen_model_t_xw(self):
        if self.model_t_xw == 'auto':
            if self.discrete_treatment:
                model_t_xw = LogisticRegressionCV(cv=WeightedStratifiedKFold(random_state=self.random_state),
                                                  random_state=self.random_state)
            else:
                model_t_xw = WeightedLassoCVWrapper(random_state=self.random_state)
        else:
            model_t_xw = clone(self.model_t_xw, safe=False)
        return _FirstStageWrapper(model_t_xw, False, self._gen_featurizer(),
                                  False, self.discrete_treatment)

    def _gen_model_t_xwz(self):
        if self.model_t_xwz == 'auto':
            if self.discrete_treatment:
                model_t_xwz = LogisticRegressionCV(cv=WeightedStratifiedKFold(random_state=self.random_state),
                                                   random_state=self.random_state)
            else:
                model_t_xwz = WeightedLassoCVWrapper(random_state=self.random_state)
        else:
            model_t_xwz = clone(self.model_t_xwz, safe=False)
        return _FirstStageWrapper(model_t_xwz, False, self._gen_featurizer(),
                                  False, self.discrete_treatment)

    def _gen_model_final(self):
        return clone(self.model_final, safe=False)

    def _gen_ortho_learner_model_nuisance(self):
        return _BaseDMLIVModelNuisance(self._gen_model_y_xw(), self._gen_model_t_xw(), self._gen_model_t_xwz())

    def _gen_ortho_learner_model_final(self):
        return _BaseDMLIVModelFinal(_FinalWrapper(self._gen_model_final(),
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