code: def test_multitask_regressor_uncertainty():
      """Test computing uncertainty for a MultitaskRegressor."""
      n_tasks = 1
      n_samples = 30
      n_features = 1
      noise = 0.1
  
      # Generate dummy dataset
      X = np.random.rand(n_samples, n_features, 1)
      y = 10 * X + np.random.normal(
          scale=noise, size=(n_samples, n_tasks, 1)
      )
      dataset = dc.data.NumpyDataset(X, y)
  
      model = dc.models.MultitaskRegressor(
          n_tasks,
          n_features,
          layer_sizes=[200],
          weight_init_stddevs=[0.1],
          batch_size=n_samples,
          dropouts=0.1,
          learning_rate=0.003,
          uncertainty=True,
      )
  
      # Fit trained model
      model.fit(dataset, nb_epoch=2500)
  
      # Predict the output and uncertainty.
      pred, std = model.predict_uncertainty(dataset)
      assert np.mean(np.abs(y - pred)) < 1.0
      assert noise < np.mean(std) < 1.0
  
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 22 for calling a global function with example usage: test_multitask_regressor_uncertainty() without return types