code: def _inner_updates(self, **kwargs):
      # Make a copy of model parameters for fast updates
      self.initial_fast_params = {
          n: deepcopy(p) for (n, p) in self.model.named_parameters()
      }
  
      # Keep reference to the initial fast params
      fast_params = self.initial_fast_params
  
      # Samples from the current batch
      batch_x, batch_y, batch_t = self.mb_x, self.mb_y, self.mb_task_id
  
      # Get batches from the buffer
      if self.clock.train_exp_counter > 0:
          buff_x, buff_y, buff_t = self.buffer.get_buffer_batch()
          mixed_x = torch.cat([batch_x, buff_x], dim=0)
          mixed_y = torch.cat([batch_y, buff_y], dim=0)
          mixed_t = torch.cat([batch_t, buff_t], dim=0)
      else:
          mixed_x, mixed_y, mixed_t = batch_x, batch_y, batch_t
  
      # Split the current batch into smaller chuncks
      bsize_data = batch_x.shape[0]
      rough_sz = math.ceil(bsize_data / self.n_inner_updates)
      self.meta_losses = [
          torch.empty(0) for _ in range(self.n_inner_updates)
      ]
  
      # Iterate through the chunks as inner-loops
      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
          fast_params = self.inner_update_step(
              fast_params, batch_x_i, batch_y_i, batch_t_i
          )
  
          # Compute meta-loss with the combination of batch and buffer samples
          logits_meta = torch.func.functional_call(
              self.model, fast_params, (mixed_x, mixed_t)
          )
          meta_loss_i = self._criterion(logits_meta, mixed_y)
          self.meta_losses[i] = meta_loss_i
  
imports: from typing import Callable, List, Sequence, Optional, Union
  from packaging.version import parse
  import warnings
  import torch
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Module, CrossEntropyLoss
  from torch.optim import Optimizer
  from torch import Tensor
  import math
  from copy import deepcopy
  from avalanche.training.plugins import SupervisedPlugin, EvaluationPlugin
  from avalanche.training.plugins.evaluation import default_evaluator
  from avalanche.training.templates import SupervisedMetaLearningTemplate
  from avalanche.training.storage_policy import ReservoirSamplingBuffer
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