class_docstr: TimeSequence feature engineering
docstr: change the input_df column order into [datetime, target, feature1, feature2, ...]
  :param input_df:
  :return:
docstr_tok: ["change","the","input_df","column","order","into","[","datetime",",","target",",","feature1",",","feature2",",","...","]",":","param","input_df",":",":","return",":"]
code: def _rearrange_data(self, input_df):
      """
      change the input_df column order into [datetime, target, feature1, feature2, ...]
      :param input_df:
      :return:
      """
      cols = input_df.columns.tolist()
      new_cols = (
          [self.dt_col]
          + self.target_col
          + [
              col
              for col in cols
              if col != self.dt_col and col not in self.target_col
          ]
      )
      rearranged_data = input_df[new_cols].copy
      return rearranged_data
  
imports: from bigdl.chronos.autots.deprecated.feature.utils import save_config
  from bigdl.chronos.autots.deprecated.feature.abstract import BaseFeatureTransformer
  from bigdl.chronos.utils import deprecated
  import sklearn
  from sklearn.preprocessing import StandardScaler
  import pandas
  import numpy
  import json
  from packaging import version
label_desc: Use image node_id 23 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj._rearrange_data(input_df) and returns: rearranged_data