code: def test_sparse(csr_container):
      # LocalOutlierFactor must support CSR inputs
      # TODO: compare results on dense and sparse data as proposed in:
      # https://github.com/scikit-learn/scikit-learn/pull/23585#discussion_r968388186
      X = csr_container(iris.data)
  
      lof = neighbors.LocalOutlierFactor(novelty=True)
      lof.fit(X)
      lof.predict(X)
      lof.score_samples(X)
      lof.decision_function(X)
  
      lof = neighbors.LocalOutlierFactor(novelty=False)
      lof.fit_predict(X)
  
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 12 for calling a global function with example usage: test_sparse(csr_container) without return types