code: def test_make_genai_metric_throws_if_grading_context_cols_wrong(
      grading_cols, example_context_cols
  ):
      with pytest.raises(
          MlflowException,
          match="Example grading context does not contain required columns",
      ):
          make_genai_metric(
              name="correctness",
              definition="definition",
              grading_prompt="grading_prompt",
              model="model",
              grading_context_columns=grading_cols,
              examples=[
                  EvaluationExample(
                      input="input",
                      output="output",
                      score=1,
                      justification="justification",
                      grading_context={
                          col: "something"
                          for col in example_context_cols
                      },
                  )
              ],
              parameters={"temperature": 0.0},
              greater_is_better=True,
              aggregations=["mean"],
          )
  
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 7 for calling a global function with example usage: test_make_genai_metric_throws_if_grading_context_cols_wrong(grading_cols, example_context_cols) without return types