code: def test_gc_binary_kappa_classification():
      """Test multiclass classification evaluation."""
      np.random.seed(1234)
      smiles = ["C", "CC", "CO", "CCC", "CCCC"]
      featurizer = dc.feat.ConvMolFeaturizer()
      X = featurizer.featurize(smiles)
      y = np.random.randint(2, size=(len(smiles),))
      dataset = dc.data.NumpyDataset(X, y)
      model = dc.models.GraphConvModel(1, mode="classification")
      # TODO: Fix this case with correct thresholding
      evaluator = Evaluator(model, dataset, [])
      multitask_scores = evaluator.compute_model_performance(
          dc.metrics.kappa_score, n_classes=2
      )
      assert len(multitask_scores) == 1
      assert multitask_scores["metric-1"] <= 1
      assert multitask_scores["metric-1"] >= -1
  
imports: import pytest
  import deepchem
  import numpy
  import sklearn
  from deepchem.utils.evaluate import Evaluator
  from deepchem.utils.evaluate import GeneratorEvaluator
label_desc: Use image node_id 20 for calling a global function with example usage: test_gc_binary_kappa_classification() without return types