code: def test_sklearn_multitask_classification_overfit():
      """Test SKLearn singletask-to-multitask overfits tiny data."""
      n_tasks = 10
      tasks = ["task%d" % task for task in range(n_tasks)]
      n_samples = 10
      n_features = 3
  
      # Generate dummy dataset
      np.random.seed(123)
      ids = np.arange(n_samples)
      X = np.random.rand(n_samples, n_features)
      y = np.random.randint(2, size=(n_samples, n_tasks))
      w = np.ones((n_samples, n_tasks))
      dataset = dc.data.DiskDataset.from_numpy(X, y, w, ids)
  
      classification_metric = dc.metrics.Metric(
          dc.metrics.roc_auc_score, task_averager=np.mean
      )
  
      def model_builder(model_dir):
          sklearn_model = RandomForestClassifier()
          return dc.models.SklearnModel(sklearn_model, model_dir)
  
      model = dc.models.SingletaskToMultitask(tasks, model_builder)
  
      # Fit trained model
      model.fit(dataset)
      model.save()
  
      # Eval model on train
      scores = model.evaluate(dataset, [classification_metric])
      assert scores[classification_metric.name] > 0.9
  
imports: import os
  import numpy
  import pytest
  from flaky import flaky
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  import deepchem
  from deepchem.models.optimizers import Adam
label_desc: Use image node_id 10 for calling a global function with example usage: test_sklearn_multitask_classification_overfit() and returns: dc