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 related attributes.
code: def _set_static_regression_attributes(self):
      """set and validate regression related attributes."""
      # if no regressors, end here
      if self.regressor_col is None:
          return
  
      # inside *.stan files, we need to distinguish regular, positive and negative regressors
      for index, reg_sign in enumerate(self._regressor_sign):
          if reg_sign == "+":
              self.num_of_positive_regressors += 1
              self.positive_regressor_col.append(
                  self.regressor_col[index]
              )
              self.positive_regressor_beta_prior.append(
                  self._regressor_beta_prior[index]
              )
              self.positive_regressor_sigma_prior.append(
                  self._regressor_sigma_prior[index]
              )
          elif reg_sign == "-":
              self.num_of_negative_regressors += 1
              self.negative_regressor_col.append(
                  self.regressor_col[index]
              )
              self.negative_regressor_beta_prior.append(
                  self._regressor_beta_prior[index]
              )
              self.negative_regressor_sigma_prior.append(
                  self._regressor_sigma_prior[index]
              )
          else:
              self.num_of_regular_regressors += 1
              self.regular_regressor_col.append(
                  self.regressor_col[index]
              )
              self.regular_regressor_beta_prior.append(
                  self._regressor_beta_prior[index]
              )
              self.regular_regressor_sigma_prior.append(
                  self._regressor_sigma_prior[index]
              )
  
      self._regressor_col = (
          self.positive_regressor_col
          + self.negative_regressor_col
          + self.regular_regressor_col
      )
  
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 6 for calling the LGTModel obj's underlying member method code with example usage: obj._set_static_regression_attributes() without return types