code: def test_y_minmax_random():
      """Test on random example"""
      n_samples = 100
      n_features = 10
      n_tasks = 10
  
      X = np.random.randn(n_samples, n_features)
      y = np.random.randn(n_samples, n_tasks)
      dataset = dc.data.NumpyDataset(X, y)
  
      minmax_transformer = dc.trans.MinMaxTransformer(
          transform_y=True, dataset=dataset
      )
      w, ids = dataset.w, dataset.ids
  
      dataset = minmax_transformer.transform(dataset)
      X_t, y_t, w_t, ids_t = (
          dataset.X,
          dataset.y,
          dataset.w,
          dataset.ids,
      )
      # Check ids are unchanged before and after transformation
      for id_elt, id_t_elt in zip(ids, ids_t):
          assert id_elt == id_t_elt
  
      # Check X is unchanged since transform_y is true
      np.testing.assert_allclose(X, X_t)
      # Check w is unchanged since transform_y is true
      np.testing.assert_allclose(w, w_t)
  
      # Check minimum and maximum values of transformed y are 0 and 1
      np.testing.assert_allclose(y_t.min(), 0.0)
      np.testing.assert_allclose(y_t.max(), 1.0)
  
      # Test if dimensionality expansion is handled correctly by untransform
      y_t = np.expand_dims(y_t, axis=-1)
      y_restored = minmax_transformer.untransform(y_t)
      assert y_restored.shape == y.shape + (1,)
      np.testing.assert_allclose(np.squeeze(y_restored, axis=-1), y)
  
imports: import os
  import numpy
  import deepchem
label_desc: Use image node_id 3 for calling a global function with example usage: test_y_minmax_random() without return types