code: def reg_func_regressors(config_regressors, model):
      """
      Regularization of regressors coefficients to induce sparsity
  
      Parameters
      ----------
          config_regressors : configure.ConfigFutureRegressors
              Configurations for user specified regressors
          model : TimeNet
              TimeNet model object
  
      Returns
      -------
          scalar
              Regularization loss
      """
      reg_regressor_loss = 0.0
      for regressor, configs in config_regressors.items():
          reg_lambda = configs.reg_lambda
          if reg_lambda is not None:
              weight = model.future_regressors.get_reg_weights(
                  regressor
              )
              reg_regressor_loss += reg_lambda * reg_func_abs(weight)
  
      return reg_regressor_loss
  
imports: from __future__ import annotations
  import logging
  import math
  import os
  import sys
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Iterable, Optional, Union
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  from neuralprophet import utils_torch
  from neuralprophet.hdays_utils import get_country_holidays
  from neuralprophet.logger import ProgressBar
label_desc: Use image node_id 9 for calling a global function with example usage: reg_func_regressors(config_regressors, model) and returns: reg_regressor_loss