code: def test_hasattr_prediction():
      # check availability of prediction methods depending on novelty value.
      X = [[1, 1], [1, 2], [2, 1]]
  
      # when novelty=True
      clf = neighbors.LocalOutlierFactor(novelty=True)
      clf.fit(X)
      assert hasattr(clf, "predict")
      assert hasattr(clf, "decision_function")
      assert hasattr(clf, "score_samples")
      assert not hasattr(clf, "fit_predict")
  
      # when novelty=False
      clf = neighbors.LocalOutlierFactor(novelty=False)
      clf.fit(X)
      assert hasattr(clf, "fit_predict")
      assert not hasattr(clf, "predict")
      assert not hasattr(clf, "decision_function")
      assert not hasattr(clf, "score_samples")
  
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 9 for calling a global function with example usage: test_hasattr_prediction() without return types