code: def check_each_var_for_stationarity(time_df, autolag, verbose=0):
      alpha = 0.05
      all_vars = 1
      copy_cols = time_df.columns.tolist()
      for each_var in copy_cols:
          timeseries = time_df[each_var].values
          dftest = smt.adfuller(timeseries, autolag=autolag)
          if verbose >= 2:
              ############################ Print Summary #####################
              output = {
                  "test_statistic": round(dftest[0], 4),
                  "pvalue": round(dftest[1], 4),
                  "n_lags": round(dftest[2], 4),
                  "n_obs": dftest[3],
              }
              p_value = output["pvalue"]
              print(
                  f'    Augmented Dickey-Fuller Test on "{each_var}"',
                  "\n   ",
                  "-" * 47,
              )
              print(
                  f" Null Hypothesis: Data has unit root. Non-Stationary."
              )
              print(f" Significance Level    = {alpha}")
              print(
                  f' Test Statistic        = {output["test_statistic"]}'
              )
              print(f' No. Lags Chosen       = {output["n_lags"]}')
  
              for key, val in dftest[4].items():
                  print(
                      f" Critical value {adjust(key)} = {round(val, 3)}"
                  )
  
              if p_value <= alpha:
                  print(
                      f" => P-Value = {p_value}. Rejecting Null Hypothesis."
                  )
                  print(f" => Series is Stationary.")
              else:
                  print(
                      f" => P-Value = {p_value}. Weak evidence to reject the Null Hypothesis."
                  )
                  print(f" => Series is Non-Stationary.")
              ####################################################################
          if dftest[1] < alpha:
              all_vars = 1 * all_vars
          else:
              all_vars = 0 * all_vars
      return all_vars
  
imports: from itertools import cycle
  import numpy
  import pandas
  import matplotlib.dates
  from itertools import cycle
  import matplotlib.pyplot
  import seaborn
  import statsmodels.tsa.api
  import pdb
  import copy
  import dask
  import dask.dataframe
  from .colors import colorful
  import copy
label_desc: Use image node_id 7 for calling a global function with example usage: check_each_var_for_stationarity(time_df, autolag, verbose) and returns: all_vars