code: def fit(self, list_of_labels):
      if not isinstance(list_of_labels, pd.Series):
          list_of_labels = pd.Series(list_of_labels)
      unique_labels = list_of_labels.unique()
      try:
          unique_labels = sorted(unique_labels)
      except TypeError:
          unique_labels = unique_labels
  
      for idx, val in enumerate(unique_labels):
          self.label_map[val] = idx
      return self
  
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 CustomLabelEncoder obj's underlying member method code with example usage: obj.fit(list_of_labels) and returns: self