code: def test_lfr_multiclass():
      """Test that the new LFR runs with >2 classes."""
      X, y = make_classification(n_informative=3, n_classes=4)
      prot_attr = np.random.randint(2, size=X.shape[:1])
      X = pd.DataFrame(X, index=pd.Series(prot_attr, name="prot_attr"))
      lfr = LearnedFairRepresentations()
      lfr.fit(X, y)
      assert lfr.coef_.shape[1] == 4
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import make_pipeline
  from aif360.algorithms.preprocessing import LFR
  from aif360.sklearn.preprocessing import LearnedFairRepresentations
  from aif360.sklearn.metrics import make_scorer, statistical_parity_difference
label_desc: Use image node_id 10 for calling a global function with example usage: test_lfr_multiclass() without return types