code: def test_fbeta_score_evaluator():
      predictions = pd.DataFrame(
          {"target": [0, 1, 0, 1], "prediction": [0.2, 0.9, 0.3, 0.8]}
      )
  
      eval_fn = fbeta_score_evaluator(
          prediction_column="prediction",
          threshold=0.5,
          beta=1,
          target_column="target",
          eval_name="eval_name",
      )
  
      result = eval_fn(predictions)
  
      assert result["eval_name"] == 1.0
  
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 9 for calling a global function with example usage: test_fbeta_score_evaluator() without return types