code: def update_parameters(
      self, model, step=None, start_step=None, end_step=None
  ):
      model = self.filter(model)
      for p_swa, p_model in zip(self.parameters(), model.parameters()):
          device = p_swa.device
          p_model_ = p_model.detach().to(device)
          if self.n_averaged == 0:
              p_swa.detach().copy_(p_model_)
          else:
              p_swa.detach().copy_(
                  self.avg_fn(
                      p_swa.detach(),
                      p_model_,
                      self.n_averaged.to(device),
                  )
              )
      self.n_averaged += 1
  
      if step is not None:
          if start_step is None:
              start_step = step
          if end_step is None:
              end_step = step
  
      if start_step is not None:
          if self.n_averaged == 1:
              self.start_step = start_step
  
      if end_step is not None:
          self.end_step = end_step
  
imports: import copy
  import warnings
  import math
  from copy import deepcopy
  import torch
  from torch.nn import Module
  from torch.optim.lr_scheduler import _LRScheduler
  from domainbed.ur_networks import URResNet
label_desc: Use image node_id 6 for calling the AveragedModel obj's underlying member method code with example usage: obj.update_parameters(model, step, start_step, end_step) without return types