code: def test_lof_integration(tmp_path):
      """Test lof detector on moons dataset.
  
      Tests lof detector on the moons dataset. Fits and infers thresholds and verifies that the detector can
      correctly detect inliers and outliers. Checks that it can be serialized using the torchscript.
      """
      lof_detector = LOF(k=18)
      X_ref, _ = make_moons(
          1001, shuffle=True, noise=0.05, random_state=None
      )
      X_ref, x_inlier = X_ref[0:1000], X_ref[1000][None]
      lof_detector.fit(X_ref)
      lof_detector.infer_threshold(X_ref, 0.1)
      result = lof_detector.predict(x_inlier)
      result = result["data"]["is_outlier"][0]
      assert not result
  
      x_outlier = np.array([[-1, 1.5]])
      result = lof_detector.predict(x_outlier)
      result = result["data"]["is_outlier"][0]
      assert result
  
      ts_lof = torch.jit.script(lof_detector.backend)
      x = torch.tensor([x_inlier[0], x_outlier[0]], dtype=torch.float32)
      y = ts_lof(x)
      assert torch.all(y == torch.tensor([False, True]))
  
      ts_lof.save(tmp_path / "lof.pt")
      lof_detector = torch.load(tmp_path / "lof.pt")
      y = lof_detector(x)
      assert torch.all(y == torch.tensor([False, True]))
  
file_path: alibi-detect/alibi_detect/od/tests/test__lof/test__lof.py
imports: import pytest
  import numpy
  import torch
  from alibi_detect.od._lof import LOF
  from alibi_detect.od.pytorch.ensemble import AverageAggregator, TopKAggregator, MaxAggregator, MinAggregator, ShiftAndScaleNormalizer, PValNormalizer
  from alibi_detect.exceptions import NotFittedError, ThresholdNotInferredError
  from sklearn.datasets import make_moons
label_desc: Use image node_id 13 for calling a global function with example usage: test_lof_integration(tmp_path) without return types