code: def test_multitask_model_evaluate():
      """Test evaluation of a multitask metric."""
      X = np.random.rand(10, 5)
      y = np.random.rand(10, 2)
      dataset = dc.data.NumpyDataset(X, y)
      model = dc.models.MultitaskRegressor(2, 5)
      multitask_scores, all_task_scores = model.evaluate(
          dataset, dc.metrics.mean_absolute_error, per_task_metrics=True
      )
      assert isinstance(multitask_scores, dict)
      assert len(multitask_scores) == 1
      assert multitask_scores["metric-1"] > 0
      assert isinstance(all_task_scores, dict)
  
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 10 for calling a global function with example usage: test_multitask_model_evaluate() without return types