discussion: Can we use our own Pytorch models in avalanche stragtegy?

I have been trying to run my own PyTorch model, which is a LightningModule Derivative, using benchmarking experience which i got from avalanche. But it gives the following error:

```
Training on experience 0
-- >> Start of training phase << --
Traceback (most recent call last):
File "/Users/akashgupta/Documents/Projects/Python/binary_deepfake_detection/model.py", line 387, in
strategy.train(experience) # Train on the current experience
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/avalanche/training/templates/base_sgd.py", line 211, in train
super().train(experiences, eval_streams, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/avalanche/training/templates/base.py", line 162, in train
self._before_training_exp(**kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/avalanche/training/templates/base_sgd.py", line 291, in _before_training_exp
self.make_train_dataloader(**kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/avalanche/training/templates/base_sgd.py", line 456, in make_train_dataloader
self.dataloader = TaskBalancedDataLoader(
^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/avalanche/benchmarks/utils/data_loader.py", line 404, in __init__
task_labels_field = getattr(data, "targets_task_labels")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ClassificationDataset' object has no attribute 'targets_task_labels'
(base) MacBook-Pro:binary_deepfake_detection akashgupta$ ;2B
```
The code which I've been using:
```python
train_dataset_wrapped = as_classification_dataset(train_dataset)
test_dataset_wrapped = as_classification_dataset(val_dataset)
train_experiences = [train_dataset_wrapped] # List of training experiences
test_experiences = [test_dataset_wrapped] # List of testing experiences

# Create a benchmark scenario from the experiences
benchmark = benchmark_from_datasets(
train=train_experiences,
test=test_experiences
)

# Initialize model, optimizer, and criterion
model = BNext4DFR(num_classes=2) # Your model
optimizer = Adam(model.parameters())
criterion = nn.CrossEntropyLoss()

# Setup the strategy
strategy = Naive(
model=model,
optimizer=optimizer,
criterion=criterion
)

# Training loop
for experience in benchmark.train_stream:
print(f"Training on experience {experience.current_experience}")
strategy.train(experience) # Train on the current experience

# Evaluation loop
for experience in benchmark.test_stream:
print(f"Testing on experience {experience.current_experience}")
strategy.eval(experience)

```

Can someone suggest how to make it work?