code: def test_deduplication_works():
      # toy sample (the last two samples are outliers)
      X = [
          [-2, -1],
          [-1, -1],
          [-1, -2],
          [1, 1],
          [1, 2],
          [2, 1],
          [6, 3],
          [4, -7],
      ]
      y = [0] * 6 + [1] * 2
      X_test = [
          [-2, -1],
          [-1, -1],
          [-1, -2],
          [1, 1],
          [1, 2],
          [2, 1],
          [10, 5],
          [5, -7],
      ]
      # Test LOF
      clf = SkopeRulesClassifier(
          random_state=rng, max_samples=1.0, max_depth_duplication=3
      )
      clf.fit(X, y)
      decision_func = clf._eval_weighted_rule_sum(X_test)
      rules_vote = clf._rules_vote(X_test)
      score_top_rules = clf._score_top_rules(X_test)
      pred = clf.predict(X_test)
      pred_score_top_rules = clf._predict_top_rules(X_test, 1)
      assert True, "deduplication works"
  
file_path: imodels/tests/skope_rules_test.py
imports: import numpy
  import pytest
  from numpy.testing import assert_array_equal, assert_no_warnings, assert_raises, suppress_warnings, assert_warns
  from sklearn.datasets import load_iris, make_blobs
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import ParameterGrid
  from sklearn.utils import check_random_state
  from imodels.rule_set.skope_rules import SkopeRulesClassifier
label_desc: Use image node_id 5 for calling a global function with example usage: test_deduplication_works() without return types