code: def test_issue_1328():
      lof = anomaly.LocalOutlierFactor()
      X = [{"a": 1, "b": 1}, {"a": 1, "b": 1}]
      for x in X:
          lof.learn_one(x)
  
file_path: river/river/anomaly/test_lof.py
imports: from __future__ import annotations
  import numpy
  import pandas
  from sklearn import neighbors
  from river import anomaly, datasets
label_desc: Use image node_id 3 for calling a global function with example usage: test_issue_1328() without return types