 Here is the Python code based on your instructions:

```python
from avalanche.benchmarks.naive import Naive
from avalanche.dataset.squad import SquadV1
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from datasets import load_dataset

class TranslationNaive(Naive):
    def preprocess(self, examples):
        return [(example['question'], example['context'], example['answer']) for example in examples]

    def postprocess(self, predictions, examples):
        return [{'question': example['question'], 'context': example['context'], 'answer': predictions[i][1]} for i, example in enumerate(examples)]

def main():
    # Load SQuAD dataset
    squad_dataset = load_dataset('squad', version='v1')
    train_dataset = squad_dataset['train']
    validation_dataset = squad_dataset['validation']

    # Load T5 model
    tokenizer = AutoTokenizer.from_pretrained('t5-base')
    model = AutoModelForSeq2SeqLM.from_pretrained('t5-base')

    # Create Avalanche benchmark
    benchmark = TranslationNaive(train_dataset, validation_dataset)

    # Train the model
    benchmark.fit(model, max_epochs=3)

    # Ask a question and print the model's answer
    question = "What is the capital of France?"
    input_encodings = tokenizer(question, return_tensors='pt')
    context_encodings = tokenizer(train_dataset[0]['context'], return_tensors='pt', padding=True, truncation=True)
    inputs = {k: v.to(device) for k, v in input_encodings.items()}
    inputs['decoder_input_ids'] = inputs['input_ids'].clone()
    inputs['decoder_input_ids'][0, tokenizer.bos_token_id: tokenizer.eos_token_id] = [train_dataset[0]['answer_start']] + list(train_dataset[0]['answer'])
    outputs = model(**inputs)
    predicted_answer = tokenizer.decode(outputs.logits[:, -1, :].argmax(dim=-1))
    print(f"Question: {question}\nAnswer: {predicted_answer}")

if __name__ == "__main__":
    main()
```

Please note that this code assumes you have the Avalanche library, HuggingFace's transformers library, and PyTorch installed. Also, it assumes that you are using a GPU for training, so you might need to adjust the code to work with a CPU if necessary.