class_docstr: Parameters
  ----------
  regressor_col : list
      Names of regressor columns, if any
  regressor_sign :  list
      list with values { '+', '-', '=' } such that
      '+' indicates regressor coefficient estimates are constrained to [0, inf).
      '-' indicates regressor coefficient estimates are constrained to (-inf, 0].
      '=' indicates regressor coefficient estimates can be any value between (-inf, inf).
      The length of `regressor_sign` must be the same length as `regressor_col`. If None,
      all elements of list will be set to '='.
  regressor_beta_prior : list
      list of prior float values for regressor coefficient betas. The length of `regressor_beta_prior`
      must be the same length as `regressor_col`. If None, use non-informative priors.
  regressor_sigma_prior : list
      list of prior float values for regressor coefficient sigmas. The length of `regressor_sigma_prior`
      must be the same length as `regressor_col`. If None, use non-informative priors.
  regression_penalty : { 'fixed_ridge', 'lasso', 'auto_ridge' }
      regression penalty method
  lasso_scale : float
      float value between [0, 1], applicable only if `regression_penalty` == 'lasso'
  auto_ridge_scale : float
      float value between [0, 1], applicable only if `regression_penalty` == 'auto_ridge'
  slope_sm_input : float
      float value between [0, 1]. A larger value puts more weight on the current slope.
      If None, the model will estimate this value.
docstr: set and validate regression penalty related attributes.
code: def _set_regression_penalty(self):
      """set and validate regression penalty related attributes."""
      regression_penalty = self.regression_penalty
      self._regression_penalty = getattr(
          RegressionPenalty, regression_penalty
      ).value
  
imports: import numpy
  import pandas
  from scipy.stats import nct
  from copy import deepcopy
  import torch
  from enum import Enum
  import warnings
  from ..constants.constants import DEFAULT_REGRESSOR_SIGN, DEFAULT_REGRESSOR_BETA, DEFAULT_REGRESSOR_SIGMA, COEFFICIENT_DF_COLS, PredictMethod, PredictionKeys, TrainingMetaKeys, PredictionMetaKeys
  from ..exceptions import IllegalArgument, ModelException, DataInputException
  from .ets import ETSModel
  from ..estimators.stan_estimator import StanEstimatorMCMC, StanEstimatorMAP
  from ..estimators.pyro_estimator import PyroEstimatorSVI
label_desc: Use image node_id 5 for calling the LGTModel obj's underlying member method code with example usage: obj._set_regression_penalty() without return types