econml/dml/dml.py [430:457]:
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                         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(self):
        if self.model_y == 'auto':
            model_y = WeightedLassoCVWrapper(random_state=self.random_state)
        else:
            model_y = clone(self.model_y, safe=False)
        return _FirstStageWrapper(model_y, True, self._gen_featurizer(),
                                  self.linear_first_stages, self.discrete_treatment)

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

    def _gen_model_final(self):
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econml/dynamic/dml/_dml.py [475:502]:
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                         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(self):
        if self.model_y == 'auto':
            model_y = WeightedLassoCVWrapper(random_state=self.random_state)
        else:
            model_y = clone(self.model_y, safe=False)
        return _FirstStageWrapper(model_y, True, self._gen_featurizer(),
                                  self.linear_first_stages, self.discrete_treatment)

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

    def _gen_model_final(self):
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