class_docstr: There are only a couple of modifications needed to
  use huggingface:
  - we add a bunch of attributes corresponding to the batch items,
      redefining mb_x and mb_y too
  - _unpack_minibatch sends the dictionary values to the GPU device
  - forward and criterion are adapted for machine translation tasks.
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code: def criterion(self):
      mb_output = self.mb_output.view(-1, self.mb_output.size(-1))
      ll = self._criterion(mb_output, self.mb_y.view(-1))
      return ll
  
imports: import datasets
  import numpy
  import torch
  import torch.nn
  from transformers import AutoTokenizer, DataCollatorForSeq2Seq, T5ForConditionalGeneration
  import avalanche
  import avalanche.training.templates.base
  from avalanche.benchmarks import CLExperience, CLScenario, CLStream
  from avalanche.benchmarks.utils import AvalancheDataset, ConstantSequence, DataAttribute
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from avalanche.benchmarks.utils.data_attribute import DataAttribute
  from avalanche.benchmarks.utils.flat_data import ConstantSequence
  from avalanche.training.plugins import ReplayPlugin
label_desc: Use image node_id 8 for calling the HGNaive obj's underlying member method code with example usage: obj.criterion() and returns: ll