benchmark_with_validation stream removes attribute 'benchmark'

🐛 **Describe the bug**
I am using `benchmark_with_validation_stream` to split my benchmark into a version where the `train_stream` is divided into `train_stream` and `valid_stream`. However, training fails as soon as I set `eval_streams=[cl_val_stream] ` in my strategy.

Error: `AttributeError: 'DatasetExperience' object has no attribute 'benchmark'`

When inspecting the streams it seems like the `train_steam`, as well as the `valid_stream`, no longer have the `benchmark` attribute, whereas `test_stream` still has it.

I think the issue here is that the generated streams from `benchmark_with_validation_stream`, so the `train_stream` and `valid_stream`, belong to EagerCLStream while the `test_stream` still belongs to NCStream.
Also the `train_stream` and `valid_stream` lose the `benchmark` attribute after calling `benchmark_with_validation_stream`, while `test_stream` retains it.

🐜 **To Reproduce**
```
cl_mnist = SplitMNIST(
n_experiences=5,
return_task_id=False,
seed=42,
fixed_class_order=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
)

cl_mnist_with_val = benchmark_with_validation_stream(
cl_mnist,
validation_size=0.1,
shuffle=True,
seed=42
)

cl_train_stream = cl_mnist_with_val.train_stream
cl_test_stream = cl_mnist_with_val.test_stream
cl_val_stream = cl_mnist_with_val.valid_stream

baseline_model = SimpleMLP(num_classes=10)

baseline_optimizer = SGD(baseline_model.parameters(), lr=0.001, momentum=0.9)
baseline_criterion = CrossEntropyLoss()

baseline_naive_strategy = Naive(
model=baseline_model,
optimizer=baseline_optimizer,
criterion=baseline_criterion,
train_mb_size=64,
train_epochs=5,
eval_mb_size=64,
eval_every=0,
evaluator=baseline_eval_plugin
)

baseline_results = []
for exp in cl_train_stream:
res = baseline_naive_strategy.train(exp, eval_streams=[cl_val_stream]) # <- Error is happening here
baseline_results.append(res)

```
🐝 **Expected behavior**
I should be able to train the model on the train set, validate it during training on the validation set and do inference on the test set afterwards. However, since `cl_val_stream` laks the `benchmark` attribute, training fails when `eval_streams=[cl_val_stream]` is used.