code: def test_overfit_modular():
      """Overfit test the pretrainer to ensure it can learn a simple task."""
      np.random.seed(123)
      torch.manual_seed(10)
      n_samples = 6
      n_feat = 3
      d_hidden = 3
      n_layers = 1
      n_tasks = 6
  
      X = np.random.rand(n_samples, n_feat)
      y = np.zeros((n_samples, n_tasks)).astype(np.float32)
      dataset = dc.data.NumpyDataset(X, y)
  
      example_model = ExampleTorchModel(
          n_feat, d_hidden, n_layers, n_tasks
      )
  
      example_model.fit(dataset, nb_epoch=1000)
      prediction = np.round(
          np.squeeze(example_model.predict_on_batch(X))
      )
      assert np.array_equal(y, prediction)
  
imports: import pytest
  import deepchem
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: test_overfit_modular() without return types