code: def test_predicted_outlier_number(expected_outliers):
      # the number of predicted outliers should be equal to the number of
      # expected outliers unless there are ties in the abnormality scores.
      X = iris.data
      n_samples = X.shape[0]
      contamination = float(expected_outliers) / n_samples
  
      clf = neighbors.LocalOutlierFactor(contamination=contamination)
      y_pred = clf.fit_predict(X)
  
      num_outliers = np.sum(y_pred != 1)
      if num_outliers != expected_outliers:
          y_dec = clf.negative_outlier_factor_
          check_outlier_corruption(
              num_outliers, expected_outliers, y_dec
          )
  
imports: import re
  from math import sqrt
  import numpy
  import pytest
  from sklearn import metrics, neighbors
  from sklearn.datasets import load_iris
  from sklearn.metrics import roc_auc_score
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_allclose, assert_array_equal
  from sklearn.utils.estimator_checks import check_outlier_corruption, parametrize_with_checks
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 11 for calling a global function with example usage: test_predicted_outlier_number(expected_outliers) without return types