code: def test_mean_prediction_evaluator():
      predictions = pd.DataFrame({"prediction": [1, 0.9, 40]})
  
      eval_fn = mean_prediction_evaluator(
          prediction_column="prediction", eval_name="eval_name"
      )
  
      result = eval_fn(predictions)
  
      assert result["eval_name"] == (1 + 0.9 + 40) / 3
  
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 2 for calling a global function with example usage: test_mean_prediction_evaluator() without return types