code: def make_regressors_features(df, config_regressors):
      """Construct arrays of all scalar regressor features
      Parameters
      ----------
          df : pd.DataFrame
              Dataframe with all values including the user specified regressors
          config_regressors : configure.ConfigFutureRegressors
              User specified regressors config
      Returns
      -------
          np.array
              All additive regressor features
          np.array
              All multiplicative regressor features
      """
      additive_regressors = pd.DataFrame()
      multiplicative_regressors = pd.DataFrame()
  
      for reg in df.columns:
          if reg in config_regressors:
              mode = config_regressors[reg].mode
              if mode == "additive":
                  additive_regressors[reg] = df[reg]
              else:
                  multiplicative_regressors[reg] = df[reg]
  
      if not additive_regressors.empty:
          additive_regressors = additive_regressors[
              sorted(additive_regressors.columns.tolist())
          ]
          additive_regressors = additive_regressors.values
      else:
          additive_regressors = None
      if not multiplicative_regressors.empty:
          multiplicative_regressors = multiplicative_regressors[
              sorted(multiplicative_regressors.columns.tolist())
          ]
          multiplicative_regressors = multiplicative_regressors.values
      else:
          multiplicative_regressors = None
  
      return additive_regressors, multiplicative_regressors
  
imports: import logging
  from collections import OrderedDict, defaultdict
  from datetime import datetime
  from typing import Optional
  import numpy
  import pandas
  import torch
  from torch.utils.data.dataset import Dataset
  from neuralprophet import configure, utils
  from neuralprophet.df_utils import get_max_num_lags
  from neuralprophet.hdays_utils import get_country_holidays
label_desc: Use image node_id 7 for calling a global function with example usage: make_regressors_features(df, config_regressors) and returns: additive_regressors,  multiplicative_regressors