code: def test_transform():
      import numpy as np
  
      data = prepare_data()
      assert len(data) == 4
      train, test, _, _ = data
      transformed = transform(train, test)
      assert len(transformed) == 2
      transformed_train, transformed_test = transformed
      assert len(transformed_train) == 112
      assert len(transformed_test) == 38
      p = np.percentile(transformed_train[:, 0], [0, 25, 50, 75, 100])
      assert len(p) == 5
      assert 0 <= p[0] < 0.05, p
      assert 0.2 < p[1] < 0.3, p
      assert 0.45 < p[2] < 0.55, p
      assert 0.7 < p[3] < 0.8, p
      assert 0.95 < p[4] <= 1.0, p
  
imports: import warnings
  from sklearn.datasets import load_iris
  from sklearn.model_selection import train_test_split
  from sklearn import preprocessing
label_desc: Use image node_id 4 for calling a global function with example usage: test_transform() without return types