code: def test_ari_grade_level():
      predictions = pd.Series(
          [
              "This is a sentence.",
              (
                  "This is a much longer and more complicated sentence than the previous one, "
                  "so this sentence should have a higher grade level score."
              ),
          ]
      )
      result = ari_grade_level().eval_fn(predictions, None, {})
      assert result.scores[0] < result.scores[1]
      assert (
          result.aggregate_results["mean"]
          == (result.scores[0] + result.scores[1]) / 2
      )
      assert (
          result.scores[0]
          < result.aggregate_results["p90"]
          < result.scores[1]
      )
      assert "variance" in result.aggregate_results
  
imports: from unittest import mock
  import pandas
  import pytest
  from mlflow.metrics import MetricValue, ari_grade_level, exact_match, f1_score, flesch_kincaid_grade_level, mae, mape, max_error, mse, ndcg_at_k, precision_at_k, precision_score, r2_score, recall_at_k, recall_score, rmse, rouge1, rouge2, rougeL, rougeLsum, toxicity
label_desc: Use image node_id 5 for calling a global function with example usage: test_ari_grade_level() without return types