def absolute_error(
        self, prompt: str, response: str, score: float
    ) -> Tuple[float, Dict[str, float]]:
        """
        Method to look up the numeric expected score from a golden set and take the difference.

        Primarily used for evaluation of model generated feedback against human feedback

        Example:
            ```python
            from trulens.core import Feedback
            from trulens.feedback import GroundTruthAgreement
            from trulens.providers.bedrock import Bedrock

            golden_set =
            {"query": "How many stomachs does a cow have?", "expected_response": "Cows' diet relies primarily on grazing.", "expected_score": 0.4},
            {"query": "Name some top dental floss brands", "expected_response": "I don't know", "expected_score": 0.8}
            ]

            bedrock = Bedrock(
                model_id="amazon.titan-text-express-v1", region_name="us-east-1"
            )
            ground_truth_collection = GroundTruthAgreement(golden_set, provider=bedrock)

            f_groundtruth = Feedback(ground_truth.absolute_error.on(Select.Record.calls[0].args.args[0]).on(Select.Record.calls[0].args.args[1]).on_output()
            ```

        """

        expected_score = self._find_score(prompt, response)
        if expected_score is not None:
            ret = abs(float(score) - float(expected_score))
            expected_score = (
                "{:.2f}".format(expected_score).rstrip("0").rstrip(".")
            )
        else:
            ret = np.nan
        return ret, {"expected score": expected_score}

        Method to look up the numeric expected score from a golden set and take the difference.

        Primarily used for evaluation of model generated feedback against human feedback

        Example:
            ```python
            from trulens.core import Feedback
            from trulens.feedback import GroundTruthAgreement
            from trulens.providers.bedrock import Bedrock

            golden_set =
            {"query": "How many stomachs does a cow have?", "expected_response": "Cows' diet relies primarily on grazing.", "expected_score": 0.4},
            {"query": "Name some top dental floss brands", "expected_response": "I don't know", "expected_score": 0.8}
            ]

            bedrock = Bedrock(
                model_id="amazon.titan-text-express-v1", region_name="us-east-1"
            )
            ground_truth_collection = GroundTruthAgreement(golden_set, provider=bedrock)

            f_groundtruth = Feedback(ground_truth.absolute_error.on(Select.Record.calls[0].args.args[0]).on(Select.Record.calls[0].args.args[1]).on_output()
            ```

        
src/feedback/trulens/feedback/groundtruth.py