reagent/model_managers/model_based/synthetic_reward.py [211:240]:
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        action_preprocessing_options = (
            self.model_manager.action_preprocessing_options or PreprocessingOptions()
        )
        action_features = [
            ffi.feature_id
            for ffi in self.model_manager.action_feature_config.float_feature_infos
        ]
        logger.info(f"action allowedlist_features: {action_features}")
        action_preprocessing_options = replace(
            action_preprocessing_options, allowedlist_features=action_features
        )
        action_normalization_parameters = identify_normalization_parameters(
            input_table_spec, InputColumn.ACTION, action_preprocessing_options
        )
        return {
            NormalizationKey.STATE: NormalizationData(
                dense_normalization_parameters=state_normalization_parameters
            ),
            NormalizationKey.ACTION: NormalizationData(
                dense_normalization_parameters=action_normalization_parameters
            ),
        }

    def query_data(
        self,
        input_table_spec: TableSpec,
        sample_range: Optional[Tuple[float, float]],
        reward_options: RewardOptions,
        data_fetcher: DataFetcher,
    ) -> Dataset:
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reagent/model_managers/parametric_dqn_base.py [155:184]:
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        action_preprocessing_options = (
            self.model_manager.action_preprocessing_options or PreprocessingOptions()
        )
        action_features = [
            ffi.feature_id
            for ffi in self.model_manager.action_feature_config.float_feature_infos
        ]
        logger.info(f"action allowedlist_features: {action_features}")
        action_preprocessing_options = replace(
            action_preprocessing_options, allowedlist_features=action_features
        )
        action_normalization_parameters = identify_normalization_parameters(
            input_table_spec, InputColumn.ACTION, action_preprocessing_options
        )
        return {
            NormalizationKey.STATE: NormalizationData(
                dense_normalization_parameters=state_normalization_parameters
            ),
            NormalizationKey.ACTION: NormalizationData(
                dense_normalization_parameters=action_normalization_parameters
            ),
        }

    def query_data(
        self,
        input_table_spec: TableSpec,
        sample_range: Optional[Tuple[float, float]],
        reward_options: RewardOptions,
        data_fetcher: DataFetcher,
    ) -> Dataset:
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