reagent/model_managers/model_based/synthetic_reward.py [182:203]:
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    @property
    def should_generate_eval_dataset(self) -> bool:
        return self.model_manager.eval_parameters.calc_cpe_in_training

    def run_feature_identification(
        self, input_table_spec: TableSpec
    ) -> Dict[str, NormalizationData]:
        state_preprocessing_options = (
            self.model_manager.state_preprocessing_options or PreprocessingOptions()
        )
        state_features = [
            ffi.feature_id
            for ffi in self.model_manager.state_feature_config.float_feature_infos
        ]
        logger.info(f"state allowedlist_features: {state_features}")
        state_preprocessing_options = replace(
            state_preprocessing_options, allowedlist_features=state_features
        )

        state_normalization_parameters = identify_normalization_parameters(
            input_table_spec, InputColumn.STATE_FEATURES, state_preprocessing_options
        )
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reagent/model_managers/parametric_dqn_base.py [130:152]:
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    @property
    def should_generate_eval_dataset(self) -> bool:
        return self.model_manager.eval_parameters.calc_cpe_in_training

    def run_feature_identification(
        self, input_table_spec: TableSpec
    ) -> Dict[str, NormalizationData]:
        # Run state feature identification
        state_preprocessing_options = (
            self.model_manager.state_preprocessing_options or PreprocessingOptions()
        )
        state_features = [
            ffi.feature_id
            for ffi in self.model_manager.state_feature_config.float_feature_infos
        ]
        logger.info(f"state allowedlist_features: {state_features}")
        state_preprocessing_options = replace(
            state_preprocessing_options, allowedlist_features=state_features
        )

        state_normalization_parameters = identify_normalization_parameters(
            input_table_spec, InputColumn.STATE_FEATURES, state_preprocessing_options
        )
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