code: def test_answer_correctness_metric():
      answer_correctness_metric = answer_correctness()
      input = "What is MLflow?"
      examples = "\n".join(
          [
              str(example)
              for example in AnswerCorrectnessMetric.default_examples
          ]
      )
  
      with mock.patch.object(
          model_utils,
          "score_model_on_payload",
          return_value=properly_formatted_openai_response1,
      ) as mock_predict_function:
          metric_value = answer_correctness_metric.eval_fn(
              pd.Series([mlflow_prediction]),
              {},
              pd.Series([input]),
              pd.Series([mlflow_ground_truth]),
          )
          assert mock_predict_function.call_count == 1
          assert (
              mock_predict_function.call_args[0][0] == "openai:/gpt-4"
          )
          assert mock_predict_function.call_args[0][1] == (
              "\nTask:\nYou must return the following fields in your response in two "
              "lines, one below the other:\nscore: Your numerical score for the model's "
              "answer_correctness "
              "based on the rubric\njustification: Your reasoning about the model's "
              "answer_correctness "
              "score\n\nYou are an impartial judge. You will be given an input that was "
              "sent to a machine\nlearning model, and you will be given an output that the model "
              "produced. You\nmay also be given additional information that was used by the model "
              "to generate the output.\n\nYour task is to determine a numerical score called "
              "answer_correctness based on the input and output.\nA definition of "
              "answer_correctness and a grading rubric are provided below.\nYou must use the "
              "grading rubric to determine your score. You must also justify your score."
              "\n\nExamples could be included below for reference. Make sure to use them as "
              "references and to\nunderstand them before completing the task.\n"
              f"\nInput:\n{input}\n"
              f"\nOutput:\n{mlflow_prediction}\n"
              "\nAdditional information used by the model:\nkey: targets\nvalue:\n"
              f"{mlflow_ground_truth}\n"
              f"\nMetric definition:\n{AnswerCorrectnessMetric.definition}\n"
              f"\nGrading rubric:\n{AnswerCorrectnessMetric.grading_prompt}\n"
              "\nExamples:\n"
              f"{examples}\n"
              "\nYou must return the "
              "following fields in your response in two lines, one below the other:\nscore: Your "
              "numerical score for the model's answer_correctness based on the rubric\n"
              "justification: Your "
              "reasoning about the model's answer_correctness score\n\nDo not add additional new "
              "lines. Do "
              "not add any other fields.\n    "
          )
          assert mock_predict_function.call_args[0][2] == {
              **AnswerCorrectnessMetric.parameters,
          }
  
      assert metric_value.scores == [3]
      assert metric_value.justifications == [openai_justification1]
  
      assert metric_value.aggregate_results == {
          "mean": 3,
          "variance": 0,
          "p90": 3,
      }
  
      with pytest.raises(
          MlflowException,
          match="Failed to find answer correctness metric for version non-existent-version",
      ):
          answer_correctness(metric_version="non-existent-version")
  
imports: import inspect
  import re
  from unittest import mock
  import numpy
  import pandas
  import pytest
  from mlflow.exceptions import MlflowException
  from mlflow.metrics.genai import EvaluationExample, model_utils
  from mlflow.metrics.genai.genai_metric import _extract_score_and_justification, _format_args_string, make_genai_metric
  from mlflow.metrics.genai.metric_definitions import answer_correctness, answer_relevance, answer_similarity, faithfulness, relevance
  from mlflow.metrics.genai.prompts.v1 import AnswerCorrectnessMetric, AnswerRelevanceMetric, AnswerSimilarityMetric, FaithfulnessMetric, RelevanceMetric
label_desc: Use image node_id 12 for calling a global function with example usage: test_answer_correctness_metric() without return types