code: def _inner_updates(self, **kwargs):
      # Create a stateless copy of the model for inner-updates
      self.fast_model = higher.patch.monkeypatch(
          self.model,
          copy_initial_weights=True,
          track_higher_grads=self.second_order,
      )
      if self.clock.train_exp_counter > 0:
          batch_x = self.mb_x[: self.train_mb_size]
          batch_y = self.mb_y[: self.train_mb_size]
          batch_t = self.mb_task_id[: self.train_mb_size]
      else:
          batch_x, batch_y, batch_t = (
              self.mb_x,
              self.mb_y,
              self.mb_task_id,
          )
  
      bsize_data = batch_x.shape[0]
      rough_sz = math.ceil(bsize_data / self.n_inner_updates)
      self.meta_losses = [0 for _ in range(self.n_inner_updates)]
  
      for i in range(self.n_inner_updates):
          batch_x_i = batch_x[i * rough_sz : (i + 1) * rough_sz]
          batch_y_i = batch_y[i * rough_sz : (i + 1) * rough_sz]
          batch_t_i = batch_t[i * rough_sz : (i + 1) * rough_sz]
  
          # We assume that samples for inner update are from the same task
          self.inner_update_step(
              self.fast_model, batch_x_i, batch_y_i, batch_t_i
          )
  
          # Compute meta-loss with the combination of batch and buffer samples
          logits_meta = avalanche_forward(
              self.fast_model, self.mb_x, self.mb_task_id
          )
          meta_loss = self._criterion(logits_meta, self.mb_y)
          self.meta_losses[i] = meta_loss
  
imports: from typing import Callable, Sequence, Optional, Union
  import torch
  import warnings
  import torch.nn
  import torch.nn.functional
  from torch.nn import Module, CrossEntropyLoss
  from torch.optim import Optimizer
  import math
  from avalanche.training.plugins import SupervisedPlugin, EvaluationPlugin
  from avalanche.training.plugins.evaluation import default_evaluator
  from avalanche.training.templates import SupervisedMetaLearningTemplate
  from avalanche.models.utils import avalanche_forward
label_desc: Use image node_id 5 for calling the LaMAML obj's underlying member method code with example usage: obj._inner_updates() without return types