code: def test_quadratic_kappa():
      actuals = np.array([4, 4, 3, 4, 4, 4, 1, 1, 2, 1])
      preds = np.array([0, 2, 1, 0, 0, 0, 1, 1, 2, 1])
      value = quadratic_kappa(actuals, preds)
      assert round(value, 3) == -0.139
  
      actuals = np.array([0, 1, 0, 1])
      preds = np.array(
          [
              [0.8, 0.1, 0.1],
              [0.7, 0.1, 0.2],
              [0.1, 0.8, 0.1],
              [0.1, 0.1, 0.8],
          ]
      )
      value = quadratic_kappa(actuals, preds)
      assert value == 0.25
  
imports: import numpy
  import pytest
  import sklearn
  from autogluon.core.metrics import confusion_matrix, log_loss, quadratic_kappa, roc_auc
  from autogluon.core.metrics.softclass_metrics import soft_log_loss
label_desc: Use image node_id 18 for calling a global function with example usage: test_quadratic_kappa() without return types