code: from skorecard.bucketers import DecisionTreeBucketer, OptimalBucketer
  from sklearn.pipeline import make_pipeline
  
  
  def test_full_pipeline(df):
      """Tests some complete pipelines."""
      X = df.drop(columns=["default"])
      y = df["default"]
  
      num_cols = ["LIMIT_BAL", "BILL_AMT1"]
      cat_cols = ["EDUCATION", "MARRIAGE"]
  
      prebucket_pipeline = make_pipeline(DecisionTreeBucketer(variables=num_cols, max_n_bins=100, min_bin_size=0.05))
  
      bucket_pipeline = make_pipeline(
          OptimalBucketer(variables=num_cols, max_n_bins=10, min_bin_size=0.05),
          OptimalBucketer(variables=cat_cols, variables_type="categorical", max_n_bins=10, min_bin_size=0.05),
      )
  
      pipe = make_pipeline(prebucket_pipeline, bucket_pipeline)
  
      pipe.fit(X, y)
      pipe.transform(X)
  
apis: sklearn.pipeline.make_pipeline