code: def transform(train, test, random_state=default_random_state):
      qt = preprocessing.QuantileTransformer(random_state=random_state)
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore", category=UserWarning)
          return (qt.fit_transform(train), qt.fit_transform(test))
  
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 3 for calling a global function with example usage: transform(train, test, random_state) and returns: qt,  qt