code: def transform(self, y):
      y = column_or_1d(y, warn=True)
  
      classes = np.unique(y)
      if len(np.intersect1d(classes, self.classes_)) < len(classes):
          diff = np.setdiff1d(classes, self.classes_)
          self.classes_ = np.hstack((self.classes_, diff))
      return np.searchsorted(self.classes_, y)[0]
  
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 2 for calling the ExtendedLabelEncoder obj's underlying member method code with example usage: obj.transform(y) and returns: np