code: def get_index(df, id_col, target_col, feature_col):
      id_index = df.columns.tolist().index(id_col)
      target_col = _to_list(target_col, "target_col", deploy_mode=True)
      feature_col = _to_list(
          feature_col, "feature_col", deploy_mode=True
      )
  
      # index of target col and feature col, will be used in scale and roll
      target_feature_index = [
          df.columns.tolist().index(i) for i in target_col + feature_col
      ]
      return id_index, target_feature_index
  
imports: import torch
  import torch.nn
  from typing import List
  from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler, RobustScaler
  from .utils import _to_list
label_desc: Use image node_id 2 for calling a global function with example usage: get_index(df, id_col, target_col, feature_col) and returns: id_index,  target_feature_index