class_docstr: Linear time regression fun and some not so linear fun.
  A modular model that models classic time-series components
      * trend
      * seasonality
      * auto-regression (as AR-Net)
      * covariates (as AR-Net)
      * apriori regressors
      * events and holidays
  by using Neural Network components.
  The Auto-regression and covariate components can be configured as a deeper network (AR-Net).
class_docstr_tok: ["Linear","time","regression","fun","and","some","not","so","linear","fun",".","A","modular","model","that","models","classic","time-series","components","*","trend","*","seasonality","*","auto-regression","(","as","AR-Net",")","*","covariates","(","as","AR-Net",")","*","apriori","regressors","*","events","and","holidays","by","using","Neural","Network","components",".","The","Auto-regression","and","covariate","components","can","be","configured","as","a","deeper","network","(","AR-Net",")","."]
docstr: Function to set the covariate weights for later interpretation in compute_components.
  This function is needed since the gradient information is not available during the predict_step
  method and attributions cannot be calculated in compute_components.
  
  :param covar_weights: _description_
  :type covar_weights: torch.Tensor
docstr_tok: ["Function","to","set","the","covariate","weights","for","later","interpretation","in","compute_components",".","This","function","is","needed","since","the","gradient","information","is","not","available","during","the","predict_step","method","and","attributions","can","not","be","calculated","in","compute_components",".",":","param","covar_weights",":","_description_",":","type","covar_weights",":","torch.Tensor"]
code: def set_covar_weights(self, covar_weights: torch.Tensor):
      """
      Function to set the covariate weights for later interpretation in compute_components.
      This function is needed since the gradient information is not available during the predict_step
      method and attributions cannot be calculated in compute_components.
  
      :param covar_weights: _description_
      :type covar_weights: torch.Tensor
      """
      self.covar_weights = covar_weights
  
imports: import logging
  import math
  from collections import OrderedDict
  from functools import reduce
  from typing import Dict, List, Optional, Union
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  import torchmetrics
  from neuralprophet import configure, np_types
  from neuralprophet.components.router import get_future_regressors, get_seasonality, get_trend
  from neuralprophet.utils import check_for_regularization, config_events_to_model_dims, reg_func_events, reg_func_regressors, reg_func_season, reg_func_trend
  from neuralprophet.utils_torch import init_parameter, interprete_model
label_desc: Use image node_id 4 for calling the TimeNet obj's underlying member method code with example usage: obj.set_covar_weights(covar_weights) without return types