 Here is the Python code based on your instructions:

```python
from avalanche.benchmarks.naive import Naive
from avalanche.benchmarks.naive.datasets import SCRDataset
from avalanche.benchmarks.naive.tasks import AbsTask
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers import T5ForConditionalGeneration, T5TokenizerFast
from torch.utils.data import DataLoader
import torch

class TranslationTask(AbsTask):
    def __init__(self, input_examples, target_examples):
        super().__init__(input_examples, target_examples)
        self.input_examples = input_examples
        self.target_examples = target_examples

    def preprocess(self, examples):
        return examples

    def postprocess(self, predictions, examples):
        return predictions

class TranslationModel(ExpertModel):
    def __init__(self, model, tokenizer):
        super().__init__()
        self.model = model
        self.tokenizer = tokenizer

    def forward(self, inputs):
        return self.model(**inputs)

def preprocess_function(examples):
    input_ids = examples['input_ids']
    attention_mask = examples['attention_mask']
    return {'input_ids': input_ids, 'attention_mask': attention_mask}

def main():
    tokenizer = T5TokenizerFast.from_pretrained('t5-base')
    model = T5ForConditionalGeneration.from_pretrained('t5-base')
    model = TranslationModel(model, tokenizer)

    dataset = SCRDataset(tokenizer, 'squad', preprocess_function)
    train_set, val_set = train_test_split(dataset, test_size=0.1, random_state=42)

    train_loader = DataLoader(train_set, batch_size=32, shuffle=True)
    val_loader = DataLoader(val_set, batch_size=32, shuffle=False)

    benchmark = Naive(
        TranslationTask,
        TranslationModel,
        train_loader,
        val_loader,
        check_model_and_optimizer(model, model.parameters())
    )

    benchmark.run(num_epochs=3, num_tasks=10, num_expertises=10)

    question = "What is the capital of France?"
    input_encodings = tokenizer(question, return_tensors='pt')
    input_ids = input_encodings['input_ids'].flatten()
    attention_mask = input_encodings['attention_mask'].flatten()

    output = model(
        {'input_ids': input_ids, 'attention_mask': attention_mask}
    )[0]
    predicted_answer = tokenizer.decode(output[torch.argmax(output)])
    print(predicted_answer)

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

This code creates a custom TranslationTask and TranslationModel classes for the SQuAD question answering task using the T5 model from HuggingFace's transformers library. The main function loads the SQuAD dataset, preprocesses it, and divides it into training and validation sets. It then initializes the T5 model, sets up the continual learning scenario with Avalanche, and trains the model on the training set. Finally, it tests the model by asking it a question and printing the model's answer.