code: def _outer_update(self, **kwargs):
      w_aft = self.model.state_dict()
  
      load_dict = {}
      for name, param in self.model.named_parameters():
          load_dict[name] = self.w_bef[name] + (
              (w_aft[name] - self.w_bef[name]) * self.gamma
          )
  
      self.model.load_state_dict(load_dict, strict=False)
  
      with torch.no_grad():
          pred = self.forward()
          self.loss = self._criterion(pred, self.mb_y)
  
imports: from copy import deepcopy
  from typing import Callable, Optional, Sequence, Union
  import torch
  import torch.nn.functional
  from torch.nn import CrossEntropyLoss, Module
  from torch.optim import Optimizer
  from avalanche.models.utils import avalanche_forward
  from avalanche.training.plugins import EvaluationPlugin, SupervisedPlugin
  from avalanche.training.plugins.evaluation import default_evaluator
  from avalanche.training.storage_policy import ReservoirSamplingBuffer
  from avalanche.training.templates import SupervisedMetaLearningTemplate
label_desc: Use image node_id 4 for calling the MER obj's underlying member method code with example usage: obj._outer_update() without return types