code: def predict_intervals(self, X, return_type=None):
      Xt = X
      for name, transform in self.steps[:-1]:
          if transform is not None:
              Xt = transform.transform(Xt)
  
      return self.steps[-1][-1].predict_intervals(
          Xt, return_type=return_type
      )
  
imports: import csv
  import datetime
  import numbers
  import os
  import pkg_resources
  import numpy
  import pandas
  import scipy
  from sklearn.datasets import load_boston
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.metaestimators import if_delegate_has_method
  from sklearn.utils import column_or_1d
  from auto_ml._version import __version__
label_desc: Use image node_id 5 for calling the ExtendedPipeline obj's underlying member method code with example usage: obj.predict_intervals(X, return_type) and returns: self