code: def main():
      # Load SQuAD datasets from HuggingFace
      data_wrap = HFTextDataWrapper(dataset_name="squad", split="train")
  
      encoder_max_len = 40
      decoder_max_len = 60
  
      """
      Define a preprocessing function (code from HuggingFace) to:
      1. Convert squad dataset to be used in a text 2 text setting
      2. Encode the question and context with the tokenizer of T5 model
      """
  
      def t2t_converter(example):
          example["input_text"] = f"question: {example['question']}"
          +f"context: {example['context']} </s>"
          example[
              "target_text"
          ] = f"{example['answers']['text'][0]} </s>"
          return example
  
      def preprocess_function(
          examples,
          encoder_max_len=encoder_max_len,
          decoder_max_len=decoder_max_len,
          tokenizer=AutoTokenizer.from_pretrained("t5-small"),
      ):
          encoder_inputs = tokenizer(
              examples["input_text"],
              truncation=True,
              return_tensors="pt",
              max_length=encoder_max_len,
              pad_to_max_length=True,
          )
  
          decoder_inputs = tokenizer(
              examples["target_text"],
              truncation=True,
              return_tensors="pt",
              max_length=decoder_max_len,
              pad_to_max_length=True,
          )
  
          input_ids = encoder_inputs["input_ids"]
          input_attention = encoder_inputs["attention_mask"]
          target_ids = decoder_inputs["input_ids"]
          target_attention = decoder_inputs["attention_mask"]
  
          outputs = {
              "input_ids": input_ids,
              "attention_mask": input_attention,
              "labels": target_ids,
              "decoder_attention_mask": target_attention,
          }
          return outputs
  
      # define the data collator to pass to the resulting avalanche dataset
      data_collator = DataCollatorForSeq2Seq(
          AutoTokenizer.from_pretrained("t5-small")
      )
      data_wrap.add_collate_function(data_collator)
  
      # download the dataset
      data_wrap.download_data()
  
      # Optional: define the columns to keep after applying the preprocessing
      # function By default, only columns added to dataset by the preprocessing
      # function are kept
      columns_list = [
          "input_ids",
          "attention_masks",
          "decoder_attention_mask",
          "labels",
      ]
      data_wrap.map_preprocess_func(
          preproc_func=t2t_converter,
          batched=False,
          columns_to_keep=columns_list,
      )
      data_wrap.map_preprocess_func(
          preproc_func=preprocess_function,
          batched=True,
          columns_to_keep=columns_list,
      )
  
      # Convert to an AvalancheDataset
      dataset = data_wrap.to_avalanche_dataset(1)
  
      # Print the type
      print(dataset, type(dataset))
  
      # Init a model
      model = T5ForConditionalGeneration.from_pretrained("t5-small")
  
      train_exps = []
      exp = CLExperience()
      exp.dataset = dataset
      train_exps.append(exp)
  
      benchmark = CLScenario(
          [
              CLStream("train", train_exps),
              # add more stream here (validation, test, out-of-domain, ...)
          ]
      )
      eval_plugin = avalanche.training.plugins.EvaluationPlugin(
          avalanche.evaluation.metrics.loss_metrics(
              epoch=True, experience=True, stream=True
          ),
          loggers=[avalanche.logging.InteractiveLogger()],
          strict_checks=False,
      )
      plugins = [ReplayPlugin(mem_size=200)]
      optimizer = torch.optim.Adam(model.parameters(), lr=2)
      # Define a Strategy
      strategy = HGNaive(
          model,
          optimizer,
          torch.nn.CrossEntropyLoss(ignore_index=-100),
          evaluator=eval_plugin,
          train_epochs=1,
          train_mb_size=10,
          plugins=plugins,
      )
      for experience in benchmark.train_stream:
          strategy.train(experience, collate_fn=data_collator)
          strategy.eval(benchmark.train_stream)
  
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 1 for calling a global function with example usage: main() and returns: example, outputs