code: def kpi_transform(dfx, kpi_combo, kpi_combo_new):
      """
      Feature transformation from continuous feature to binned features for a list of features
  
      Args
      ----
  
      dfx : DataFrame
          DataFrame containing the features.
  
      kpi_combo : list of string
          List of feature names to be transformed
  
      kpi_combo_new : list of string
          List of new feature names to be assigned to the transformed features.
  
      Returns
      -------
      dfx : DataFrame
          Updated DataFrame containing the new features.
      """
      for j in range(len(kpi_combo)):
          if type(dfx[kpi_combo[j]].values[0]) is str:
              dfx[kpi_combo_new[j]] = dfx[kpi_combo[j]].values
              dfx[kpi_combo_new[j]] = cat_group(
                  dfx=dfx, kpix=kpi_combo_new[j]
              )
          else:
              if len(kpi_combo) > 1:
                  dfx[kpi_combo_new[j]] = cat_continuous(
                      dfx[kpi_combo[j]].values, granularity="Low"
                  )
              else:
                  dfx[kpi_combo_new[j]] = cat_continuous(
                      dfx[kpi_combo[j]].values, granularity="High"
                  )
      return dfx
  
file_path: causalml/causalml/inference/tree/utils.py
imports: import time
  from typing import Callable
  import numpy
  import pandas
label_desc: Use image node_id 5 for calling a global function with example usage: kpi_transform(dfx, kpi_combo, kpi_combo_new) and returns: dfx