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: Computes events component of the model
  Parameters
  ----------
      features : torch.Tensor, float
          Features (either additive or multiplicative) related to event component dims (batch, n_forecasts,
          n_features)
      params : nn.Parameter
          Params (either additive or multiplicative) related to events
      indices : list of int
          Indices in the feature tensors related to a particular event
  Returns
  -------
      torch.Tensor
          Forecast component of dims (batch, n_forecasts)
docstr_tok: ["Computes","events","component","of","the","model","Parameters","--","--","--","--","--","features",":","torch.Tensor",",","float","Features","(","either","additive","or","multiplicative",")","related","to","event","component","dims","(","batch",",","n_forecasts",",","n_features",")","params",":","nn.Parameter","Params","(","either","additive","or","multiplicative",")","related","to","events","indices",":","list","of","int","Indices","in","the","feature","tensors","related","to","a","particular","event","Returns","--","--","--","-","torch.Tensor","Forecast","component","of","dims","(","batch",",","n_forecasts",")"]
code: def scalar_features_effects(
      self, features: torch.Tensor, params: nn.Parameter, indices=None
  ) -> torch.Tensor:
      """
      Computes events component of the model
      Parameters
      ----------
          features : torch.Tensor, float
              Features (either additive or multiplicative) related to event component dims (batch, n_forecasts,
              n_features)
          params : nn.Parameter
              Params (either additive or multiplicative) related to events
          indices : list of int
              Indices in the feature tensors related to a particular event
      Returns
      -------
          torch.Tensor
              Forecast component of dims (batch, n_forecasts)
      """
      if indices is not None:
          features = features[:, :, indices]
          params = params[:, indices]
  
      return torch.sum(
          features.unsqueeze(dim=2)
          * params.unsqueeze(dim=0).unsqueeze(dim=0),
          dim=-1,
      )
  
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 7 for calling the TimeNet obj's underlying member method code with example usage: obj.scalar_features_effects(features, params, indices) and returns: torch