code: def __init__(
      self, mem_size=100, batch_size_mem=10, device=torch.device("cpu")
  ):
      self.storage_policy = ReservoirSamplingBuffer(max_size=mem_size)
      self.batch_size_mem = batch_size_mem
      self.device = device
  
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 1 to create a new MERBuffer object  with example: obj = MERBuffer(mem_size, batch_size_mem, device)