causalml/inference/iv/drivlearner.py [394:411]:
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        te = self.predict(
            X, treatment=treatment, y=y, return_components=return_components
        )

        if not return_ci:
            return te
        else:
            t_groups_global = self.t_groups
            _classes_global = self._classes
            models_mu_c_global = deepcopy(self.models_mu_c)
            models_mu_t_global = deepcopy(self.models_mu_t)
            models_tau_global = deepcopy(self.models_tau)
            te_bootstraps = np.zeros(
                shape=(X.shape[0], self.t_groups.shape[0], n_bootstraps)
            )

            logger.info("Bootstrap Confidence Intervals")
            for i in tqdm(range(n_bootstraps)):
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causalml/inference/meta/drlearner.py [301:318]:
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        te = self.predict(
            X, treatment=treatment, y=y, return_components=return_components
        )

        if not return_ci:
            return te
        else:
            t_groups_global = self.t_groups
            _classes_global = self._classes
            models_mu_c_global = deepcopy(self.models_mu_c)
            models_mu_t_global = deepcopy(self.models_mu_t)
            models_tau_global = deepcopy(self.models_tau)
            te_bootstraps = np.zeros(
                shape=(X.shape[0], self.t_groups.shape[0], n_bootstraps)
            )

            logger.info("Bootstrap Confidence Intervals")
            for i in tqdm(range(n_bootstraps)):
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