code: def test_model_evaluate_dc_metric():
      """Test a model evaluate on a dataset."""
      X = np.random.rand(10, 5)
      y = np.random.rand(10, 1)
      dataset = dc.data.NumpyDataset(X, y)
      model = dc.models.MultitaskRegressor(1, 5)
      metric = dc.metrics.Metric(dc.metrics.mae_score)
      multitask_scores = model.evaluate(dataset, metric, [])
      assert isinstance(multitask_scores, dict)
      assert len(multitask_scores) == 1
      assert multitask_scores["mae_score"] > 0
  
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 8 for calling a global function with example usage: test_model_evaluate_dc_metric() without return types