code: def test_lof_novelty_false():
      # Test chaining KNeighborsTransformer and LocalOutlierFactor
      n_neighbors = 4
  
      rng = np.random.RandomState(0)
      X = rng.randn(40, 2)
  
      # compare the chained version and the compact version
      est_chain = make_pipeline(
          KNeighborsTransformer(
              n_neighbors=n_neighbors, mode="distance"
          ),
          LocalOutlierFactor(
              metric="precomputed",
              n_neighbors=n_neighbors,
              novelty=False,
              contamination="auto",
          ),
      )
      est_compact = LocalOutlierFactor(
          n_neighbors=n_neighbors, novelty=False, contamination="auto"
      )
  
      pred_chain = est_chain.fit_predict(X)
      pred_compact = est_compact.fit_predict(X)
      assert_array_almost_equal(pred_chain, pred_compact)
  
file_path: scikit-learn/sklearn/neighbors/tests/test_neighbors_pipeline.py
imports: import numpy
  from sklearn.base import clone
  from sklearn.cluster import DBSCAN, SpectralClustering
  from sklearn.cluster.tests.common import generate_clustered_data
  from sklearn.datasets import make_blobs
  from sklearn.manifold import TSNE, Isomap, SpectralEmbedding
  from sklearn.neighbors import KNeighborsRegressor, KNeighborsTransformer, LocalOutlierFactor, RadiusNeighborsRegressor, RadiusNeighborsTransformer
  from sklearn.pipeline import make_pipeline
  from sklearn.utils._testing import assert_array_almost_equal
label_desc: Use image node_id 6 for calling a global function with example usage: test_lof_novelty_false() without return types