code: def test_score_samples(global_dtype):
      X_train = np.asarray([[1, 1], [1, 2], [2, 1]], dtype=global_dtype)
      X_test = np.asarray([[2.0, 2.0]], dtype=global_dtype)
      clf1 = neighbors.LocalOutlierFactor(
          n_neighbors=2, contamination=0.1, novelty=True
      ).fit(X_train)
      clf2 = neighbors.LocalOutlierFactor(
          n_neighbors=2, novelty=True
      ).fit(X_train)
  
      clf1_scores = clf1.score_samples(X_test)
      clf1_decisions = clf1.decision_function(X_test)
  
      clf2_scores = clf2.score_samples(X_test)
      clf2_decisions = clf2.decision_function(X_test)
  
      assert_allclose(
          clf1_scores,
          clf1_decisions + clf1.offset_,
      )
      assert_allclose(
          clf2_scores,
          clf2_decisions + clf2.offset_,
      )
      assert_allclose(clf1_scores, clf2_scores)
  
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 6 for calling a global function with example usage: test_score_samples(global_dtype) without return types