code: def multiple_lags(df, start=1, end=3, columns=None):
      if not columns:
          columns = df.columns.to_list()
      lags = range(start, end + 1)  # Just two lags for demonstration.
  
      df = df.assign(
          **{
              "{}_t_{}".format(col, t): df[col].shift(t)
              for t in lags
              for col in columns
          }
      )
      return df
  
imports: import pandas
  import numpy
  import statsmodels.api
  from scipy import signal, integrate
  from pykalman import UnscentedKalmanFilter
  from tsaug import *
  from fbprophet import Prophet
  import pylab
  from seasonal.periodogram import periodogram
label_desc: Use image node_id 23 for calling a global function with example usage: multiple_lags(df, start, end, columns) and returns: df