code: def test_exponential_coefficient_evaluator():
      a1 = -10
      a0 = -2
  
      prediction = np.array([0.0, 0.02, 0.04, 0.06, 0.08, 0.1])
  
      predictions = pd.DataFrame(
          dict(
              prediction=prediction, target=np.exp(a0 + a1 * prediction)
          )
      )
  
      result = exponential_coefficient_evaluator(predictions)
  
      assert result[
          "exponential_coefficient_evaluator__target"
      ] == pytest.approx(a1)
  
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 23 for calling a global function with example usage: test_exponential_coefficient_evaluator() without return types