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: Compute all covariate components.
  Parameters
  ----------
      covariates : dict(torch.Tensor, float)
          dict of named covariates (keys) with their features (values)
          dims of each dict value: (batch, n_lags)
  Returns
  -------
      torch.Tensor
          Forecast component of dims (batch, n_forecasts, quantiles)
docstr_tok: ["Compute","all","covariate","components",".","Parameters","--","--","--","--","--","covariates",":","dict","(","torch.Tensor",",","float",")","dict","of","named","covariates","(","keys",")","with","their","features","(","values",")","dims","of","each","dict","value",":","(","batch",",","n_lags",")","Returns","--","--","--","-","torch.Tensor","Forecast","component","of","dims","(","batch",",","n_forecasts",",","quantiles",")"]
code: def forward_covar_net(self, covariates):
      """Compute all covariate components.
      Parameters
      ----------
          covariates : dict(torch.Tensor, float)
              dict of named covariates (keys) with their features (values)
              dims of each dict value: (batch, n_lags)
      Returns
      -------
          torch.Tensor
              Forecast component of dims (batch, n_forecasts, quantiles)
      """
      # Concat covariates into one tensor)
      if isinstance(covariates, dict):
          x = torch.cat(
              [covar for _, covar in covariates.items()], axis=1
          )
      else:
          x = covariates
      for i in range(len(self.lagged_reg_layers) + 1):
          if i > 0:
              x = nn.functional.relu(x)
          x = self.covar_net[i](x)
  
      # segment the last dimension to match the quantiles
      x = x.reshape(x.shape[0], self.n_forecasts, len(self.quantiles))
      return x
  
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 9 for calling the TimeNet obj's underlying member method code with example usage: obj.forward_covar_net(covariates) and returns: x