code: def test_multitask_classification_overfit():
      """Test MultitaskClassifier overfits tiny data."""
      n_tasks = 10
      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.zeros((n_samples, n_tasks))
      w = np.ones((n_samples, n_tasks))
      dataset = dc.data.NumpyDataset(X, y, w, ids)
  
      classification_metric = dc.metrics.Metric(
          dc.metrics.accuracy_score,
          task_averager=np.mean,
          n_tasks=n_tasks,
      )
      model = dc.models.MultitaskClassifier(
          n_tasks,
          n_features,
          dropouts=[0.0],
          weight_init_stddevs=[0.1],
          batch_size=n_samples,
          optimizer=Adam(learning_rate=0.0003, beta1=0.9, beta2=0.999),
      )
  
      # Fit trained model
      model.fit(dataset)
  
      # 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 11 for calling a global function with example usage: test_multitask_classification_overfit() without return types