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: Retrieve the weights of event features given the name
  Parameters
  ----------
      name : str
          Event name
  Returns
  -------
      OrderedDict
          Dict of the weights of all offsets corresponding to a particular event
docstr_tok: ["Retrieve","the","weights","of","event","features","given","the","name","Parameters","--","--","--","--","--","name",":","str","Event","name","Returns","--","--","--","-","OrderedDict","Dict","of","the","weights","of","all","offsets","corresponding","to","a","particular","event"]
code: def get_event_weights(self, name: str) -> Dict[str, torch.Tensor]:
      """
      Retrieve the weights of event features given the name
      Parameters
      ----------
          name : str
              Event name
      Returns
      -------
          OrderedDict
              Dict of the weights of all offsets corresponding to a particular event
      """
  
      event_dims = self.events_dims[name]
      mode = event_dims["mode"]
  
      if mode == "multiplicative":
          event_params = self.event_params["multiplicative"]
      else:
          assert mode == "additive"
          event_params = self.event_params["additive"]
  
      event_param_dict = OrderedDict({})
      for event_delim, indices in zip(
          event_dims["event_delim"], event_dims["event_indices"]
      ):
          event_param_dict[event_delim] = event_params[
              :, indices : (indices + 1)
          ]
      return event_param_dict
  
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 5 for calling the TimeNet obj's underlying member method code with example usage: obj.get_event_weights(name) and returns: event_param_dict