code: def test_split_evaluator():
      predictions = pd.DataFrame(
          {
              "split_col_a": [1, 1, 0],
              "split_col_b": [2, 0, 0],
              "target": [0, 1, 2],
              "prediction": [0.5, 0.9, 1.5],
          }
      )
  
      base_eval = mean_prediction_evaluator
      split_eval = split_evaluator(
          eval_fn=base_eval, split_col="split_col_a", split_values=[1]
      )
  
      result = split_evaluator(
          predictions, split_eval, "split_col_b", [2]
      )
  
      assert (
          result["split_evaluator__split_col_b_2"][
              "split_evaluator__split_col_a_1"
          ]["mean_evaluator__prediction"]
          == 0.5
      )
  
imports: import sys
  import string
  import numpy
  import pandas
  import pytest
  from fklearn.validation.evaluators import auc_evaluator, brier_score_evaluator, combined_evaluators, correlation_evaluator, expected_calibration_error_evaluator, fbeta_score_evaluator, hash_evaluator, logloss_evaluator, mean_prediction_evaluator, mse_evaluator, permutation_evaluator, pr_auc_evaluator, precision_evaluator, r2_evaluator, recall_evaluator, roc_auc_evaluator, spearman_evaluator, linear_coefficient_evaluator, ndcg_evaluator, split_evaluator, temporal_split_evaluator, exponential_coefficient_evaluator, logistic_coefficient_evaluator
label_desc: Use image node_id 19 for calling a global function with example usage: test_split_evaluator() without return types