code: def __init__(
      self,
      steps,
      keep_cat_features=False,
      name=None,
      training_features=None,
  ):
      super(self.__class__, self).__init__(steps)
      self.keep_cat_features = keep_cat_features
      self.__versions__ = get_versions()
      self.name = name
      self.feature_importances_ = None
      self.training_features = training_features
  
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 1 to create a new ExtendedPipeline object from inherited base classes: Pipeline with example: obj = ExtendedPipeline(steps, keep_cat_features, name, training_features)