code: def test_check_fairness_multiclass_classification():
      # loading dataset
      data = pycaret.datasets.get_data("iris")
  
      # initialize setup
      pycaret.classification.setup(
          data,
          target="species",
          html=False,
          n_jobs=1,
          train_size=0.8,
      )
  
      # train model
      lightgbm = pycaret.classification.create_model(
          "lightgbm", cross_validation=False
      )
  
      # check fairness
      lightgbm_fairness = pycaret.classification.check_fairness(
          lightgbm, ["sepal_length"]
      )
      assert isinstance(lightgbm_fairness, pd.DataFrame)
  
imports: import pandas
  import pycaret.classification
  import pycaret.datasets
  import pycaret.regression
label_desc: Use image node_id 2 for calling a global function with example usage: test_check_fairness_multiclass_classification() without return types