code: def test_singletask_robust_multitask_classification():
      """Test robust multitask singletask classification."""
      n_tasks = 1
      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)
  
      _ = dc.metrics.Metric(
          dc.metrics.accuracy_score, task_averager=np.mean
      )
      model = dc.models.RobustMultitaskClassifier(
          n_tasks,
          n_features,
          layer_sizes=[50],
          bypass_layer_sizes=[10],
          dropouts=[0.0],
          learning_rate=0.003,
          weight_init_stddevs=[0.1],
          batch_size=n_samples,
      )
  
      # Fit trained model
      model.fit(dataset, nb_epoch=1)
  
imports: import numpy
  import deepchem
  import pytest
label_desc: Use image node_id 1 for calling a global function with example usage: test_singletask_robust_multitask_classification() without return types