code: def transform(self, in_vals):
      return_vals = []
      for val in in_vals:
          if not isinstance(val, str):
              if (
                  isinstance(val, float)
                  or isinstance(val, int)
                  or val is None
                  or isinstance(val, np.generic)
              ):
                  val = str(val)
              else:
                  val = val.encode("utf-8").decode("utf-8")
  
          if val not in self.label_map:
              self.label_map[val] = len(self.label_map.keys())
          return_vals.append(self.label_map[val])
  
      if len(in_vals) == 1:
          return return_vals[0]
      else:
          return return_vals
  
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 3 for calling the CustomLabelEncoder obj's underlying member method code with example usage: obj.transform(in_vals) and returns: return_vals, return_vals