code: def change_to_datetime_index(dft, ts_column):
      dft = copy.deepcopy(dft)
      if isinstance(dft, pd.Series) or isinstance(dft, pd.DataFrame):
          try:
              ### If ts_column is not a string column, then set its format to an empty string ##
              str_format = ""
              ############### Check if it has an index or a column with the name of train time series column ####
  
              if ts_column in dft.columns:
                  print(
                      "    %s column exists in given train data..."
                      % ts_column
                  )
                  str_first_value = dft[ts_column].values[0]
                  str_values = dft[ts_column].values[
                      :12
                  ]  ### we want to test a big sample of them
                  if type(str_first_value) == str:
                      ### if it is an object column, convert ts_column into datetime and then set as index
                      str_format = infer_date_time_format(str_values)
                      if str_format:
                          str_format = str_format[0]
                          ts_index = pd.to_datetime(
                              dft.pop(ts_column), format=str_format
                          )
                      else:
                          ts_index = pd.to_datetime(dft.pop(ts_column))
                      dft.index = ts_index
                  elif (
                      type(str_first_value) == pd.Timestamp
                      or type(str_first_value) == np.datetime64
                  ):
                      ### if it is a datetime column, then set it as index
                      ### if it a datetime index, then just set the index as is
                      ts_index = dft.pop(ts_column)
                      dft.index = ts_index
                  elif type(str_first_value) in [
                      np.int8,
                      np.int16,
                      np.int32,
                      np.int64,
                  ]:
                      ### if it is an integer column, convert ts_column into datetime and then set as index
                      ts_index = pd.to_datetime(dft.pop(ts_column))
                      dft.index = ts_index
                  else:
                      print(
                          "    Type of time series column %s is float or unknown. Must be string or datetime. Please check input and try again."
                          % ts_column
                      )
                      return
              elif ts_column in dft.index.name:
                  print(
                      "    train time series %s column is the index on test data..."
                      % ts_column
                  )
                  ts_index = dft.index
                  str_first_value = ts_index[0]
                  str_values = ts_index[:12]
                  if type(str_first_value) == str:
                      ### if index is in string format, you must infer its datetime string format and then set datetime index
                      str_format = infer_date_time_format(str_values)
                      if str_format:
                          str_format = str_format[0]
                          ts_index = pd.to_datetime(
                              ts_index, format=str_format
                          )
                      else:
                          ts_index = pd.to_datetime(ts_index)
                      dft.index = ts_index
                  elif (
                      type(ts_index)
                      == pd.core.indexes.datetimes.DatetimeIndex
                  ):
                      ### if dft already has a datetime index, leave it as it is
                      pass
                  elif (
                      type(ts_index) == pd.DatetimeIndex
                      or dft.index.dtype == "<M8[ns]"
                  ):
                      ### if dft already has a datatime index, leave it as is
                      pass
                  elif type(str_first_value) in [
                      np.int8,
                      np.int16,
                      np.int32,
                      np.int64,
                  ]:
                      ### if it is not a datetime index, then convert it to datetime and set the index
                      ts_index = pd.to_datetime(ts_index)
                      dft.index = ts_index
                  else:
                      print(
                          "    Type of index is unknown or float. It must be datetime or string. Please check input and try again."
                      )
                      return
              else:
                  print(
                      f"    (Error) Cannot find '{ts_column}' (or index) in given data."
                  )
                  return None
          except:
              print(
                  "    Trying to convert time series column %s into index erroring. Please check input and try again."
                  % ts_column
              )
              return
      elif type(dft) == dask.dataframe.core.DataFrame:
          str_format = ""
          if ts_column in dft.columns:
              print(
                  "    %s column exists in dask data frame..."
                  % ts_column
              )
              str_first_value = dft[ts_column].compute()[0]
              dft.index = dd.to_datetime(dft[ts_column].compute())
              dft = dft.drop(ts_column, axis=1)
          elif ts_column in dft.index.name:
              print(
                  "    train index %s is already a time series index. Continuing..."
                  % ts_column
              )
          else:
              print(
                  f"    (Error) Model to be used for prediction 'ML'. Hence, input df must have a column (or index) called '{ts_column}' corresponding to the original ts_index column passed during training. No predictions will be made."
              )
              return None
      else:
          print(
              "    Unable to detect type of data. Please check your input and try again"
          )
          return
      return dft, str_format
  
imports: from typing import List
  import numpy
  import pandas
  import copy
  import pdb
  from sklearn.model_selection import TimeSeriesSplit
  import dask
  import dask.dataframe
  import datetime
  from datetime import datetime, date, time
label_desc: Use image node_id 4 for calling a global function with example usage: change_to_datetime_index(dft, ts_column) and returns: dft,  str_format, None, None