code: def test_different_scorer():
      X, y = make_classification(n_samples=100, random_state=42)
      X_val, y_val = make_classification(n_samples=25, random_state=123)
      pool = AdaBoostClassifier(n_estimators=10).fit(X, y)
      performances = []
      for clf in pool:
          preds = clf.predict_proba(X_val)
          performances.append(log_loss(y_val.ravel(), preds[:, -1]))
      id_best = np.argsort(performances)
      ss = StaticSelection(
          pool_classifiers=pool, scoring="neg_log_loss"
      )
      ss.fit(X_val, y_val)
      assert (
          id_best[: ss.n_classifiers_ensemble_] == ss.clf_indices_
      ).all()
  
imports: import numpy
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.exceptions import NotFittedError
  from sklearn.metrics import log_loss
  from sklearn.utils.estimator_checks import check_estimator
  from deslib.static.static_selection import StaticSelection
label_desc: Use image node_id 11 for calling a global function with example usage: test_different_scorer() without return types