code: def main():
      # Load SQuAD datasets from HuggingFace
      squad_tr = load_dataset("squad", split="train")
      squad_val = load_dataset("squad", split="validation")
  
      tokenizer = AutoTokenizer.from_pretrained("t5-small")
      encoder_max_len = tokenizer.model_max_length
      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,
      ):
          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
  
      # Map the preprocessing function to the dataset so that it's applied to
      # all examples
      squad_tr = squad_tr.map(t2t_converter)
      squad_tr = squad_tr.map(preprocess_function, batched=True)
      squad_tr = squad_tr.remove_columns(
          [
              "id",
              "title",
              "context",
              "question",
              "answers",
              "input_text",
              "target_text",
          ]
      )
      squad_val = squad_val.map(t2t_converter)
      squad_val = squad_val.map(preprocess_function, batched=True)
      # ,' input_text', 'target_text'])
      squad_val = squad_val.remove_columns(
          ["id", "title", "context", "question", "answers"]
      )
  
      model = T5ForConditionalGeneration.from_pretrained("t5-small")
      # Use a standard data collator for QA
      data_collator = DataCollatorForSeq2Seq(tokenizer)
  
      train_exps = []
      for i in range(0, 2):
          # We use very small experiences only to showcase the library.
          # Adapt this to your own benchmark
          exp_data = squad_tr.select(range(30 * i, 30 * (i + 1)))
          tl = DataAttribute(
              ConstantSequence(i, len(exp_data)), "targets_task_labels"
          )
  
          exp = CLExperience()
          exp.dataset = AvalancheDataset(
              [exp_data], data_attributes=[tl], collate_fn=data_collator
          )
          train_exps.append(exp)
      tl = DataAttribute(
          ConstantSequence(2, len(squad_val)), "targets_task_labels"
      )
      val_exp = CLExperience()
      val_exp.dataset = AvalancheDataset(
          [squad_val], data_attributes=[tl], collate_fn=data_collator
      )
      val_exp = [val_exp]
  
      benchmark = CLScenario(
          [
              CLStream("train", train_exps),
              CLStream("valid", val_exp)
              # 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)
      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)
  
      # Test the model:
      model.eval()
      question = "Which libraries is Avalanche based upon?"
      context = """
      Avalanche is an End-to-End Continual Learning Library 
      based on PyTorch, born within ContinualAI with the goal of providing 
      a shared and collaborative open-source (MIT licensed) codebase for fast
      prototyping, training and reproducible evaluation of continual learning
      algorithms."
      """
  
      input_text = f"answer_me: {question} context: {context} </s>"
      encoded_query = tokenizer(
          input_text,
          return_tensors="pt",
          pad_to_max_length=True,
          truncation=True,
          max_length=250,
      )
      input_ids = encoded_query["input_ids"]
      attention_mask = encoded_query["attention_mask"]
      generated_answer = model.generate(
          input_ids,
          attention_mask=attention_mask,
          max_length=50,
          top_p=0.95,
          top_k=50,
          repetition_penalty=2.0,
      )
  
      decoded_answer = tokenizer.batch_decode(
          generated_answer, skip_special_tokens=True
      )
      print(f"Answer: {decoded_answer}")
  
imports: from avalanche.benchmarks.utils import DataAttribute, ConstantSequence
  from avalanche.training.plugins import ReplayPlugin
  from transformers import DataCollatorForSeq2Seq
  import torch
  import avalanche
  import torch.nn
  from avalanche.benchmarks import CLScenario, CLStream, CLExperience
  import avalanche.training.templates.base
  from avalanche.benchmarks.utils import AvalancheDataset
  from transformers import AutoTokenizer
  from transformers import T5ForConditionalGeneration
  from datasets import load_dataset
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: example, outputs