code: def test_novelty_training_scores(global_dtype):
      # check that the scores of the training samples are still accessible
      # when novelty=True through the negative_outlier_factor_ attribute
      X = iris.data.astype(global_dtype)
  
      # fit with novelty=False
      clf_1 = neighbors.LocalOutlierFactor()
      clf_1.fit(X)
      scores_1 = clf_1.negative_outlier_factor_
  
      # fit with novelty=True
      clf_2 = neighbors.LocalOutlierFactor(novelty=True)
      clf_2.fit(X)
      scores_2 = clf_2.negative_outlier_factor_
  
      assert_allclose(scores_1, scores_2)
  
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 8 for calling a global function with example usage: test_novelty_training_scores(global_dtype) without return types