code: def _inner_updates(self, **kwargs):
      for inner_itr in range(self.n_inner_steps):
          x, y, t = self.mb_x, self.mb_y, self.mb_task_id
          x, y, t = self.buffer.get_batch(x, y, t)
  
          # Inner updates
          w_bef_t = deepcopy(self.model.state_dict())
          for idx in range(x.shape[0]):
              x_b = x[idx].unsqueeze(0)
              y_b = y[idx].unsqueeze(0)
              t_b = t[idx].unsqueeze(0)
              self.model.zero_grad()
              pred = avalanche_forward(self.model, x_b, t_b)
              loss = self._criterion(pred, y_b)
              loss.backward()
              self.optimizer.step()
  
          # Within-batch Reptile update
          w_aft_t = self.model.state_dict()
          load_dict = {}
          for name, param in self.model.named_parameters():
              load_dict[name] = w_bef_t[name] + (
                  (w_aft_t[name] - w_bef_t[name]) * self.beta
              )
  
          self.model.load_state_dict(load_dict, strict=False)
  
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 3 for calling the MER obj's underlying member method code with example usage: obj._inner_updates() without return types