code: def test_clipping_X_transformer():
      """Test clipping transformer on X of singletask dataset."""
      n_samples = 10
      n_features = 3
      n_tasks = 1
      ids = np.arange(n_samples)
      X = np.ones((n_samples, n_features))
      target = 5.0 * X
      X *= 6.0
      y = np.zeros((n_samples, n_tasks))
      w = np.ones((n_samples, n_tasks))
      dataset = dc.data.NumpyDataset(X, y, w, ids)
      transformer = dc.trans.ClippingTransformer(
          transform_X=True, x_max=5.0
      )
      clipped_dataset = transformer.transform(dataset)
      X_t, y_t, w_t, ids_t = (
          clipped_dataset.X,
          clipped_dataset.y,
          clipped_dataset.w,
          clipped_dataset.ids,
      )
      # Check ids are unchanged.
      for id_elt, id_t_elt in zip(ids, ids_t):
          assert id_elt == id_t_elt
      # Check y is unchanged since this is an X transformer
      np.testing.assert_allclose(y, y_t)
      # Check w is unchanged since this is an X transformer
      np.testing.assert_allclose(w, w_t)
      # Check X is now holding the proper values when sorted.
      np.testing.assert_allclose(X_t, target)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_clipping_X_transformer() without return types