code: def get_batch(self, x, y, t):
      if len(self) == 0:
          return x, y, t
  
      bsize = min(len(self), self.batch_size_mem)
      rnd_ind = torch.randperm(len(self))[:bsize].tolist()
      buff_x = torch.cat(
          [
              self.storage_policy.buffer[i][0].unsqueeze(0)
              for i in rnd_ind
          ]
      ).to(self.device)
      buff_y = torch.LongTensor(
          [self.storage_policy.buffer[i][1] for i in rnd_ind]
      ).to(self.device)
      buff_t = torch.LongTensor(
          [self.storage_policy.buffer[i][2] for i in rnd_ind]
      ).to(self.device)
  
      mixed_x = torch.cat([x, buff_x], dim=0)
      mixed_y = torch.cat([y, buff_y], dim=0)
      mixed_t = torch.cat([t, buff_t], dim=0)
  
      return mixed_x, mixed_y, mixed_t
  
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 MERBuffer obj's underlying member method code with example usage: obj.get_batch(x, y, t) and returns: mixed_x,  mixed_y,  mixed_t, x,  y,  t