

```python
import argparse
import torch
from torch.nn import CrossEntropyLoss
from torch.optim import SGD
from torchvision import transforms
from avalanche.benchmarks.classic import MNIST
from avalanche.evaluation.metrics import (
    accuracy_metrics,
    loss_metrics,
    forgetting_metrics,
    cpu_usage_metrics,
    timing_metrics,
    ram_usage_metrics,
    disk_usage_metrics,
    MAC_metrics,
    bwt_metrics,
    confusion_matrix_metrics,
    labels_repartition_metrics,
)
from avalanche.logging import (
    TextLogger,
    InteractiveLogger,
    CSVLogger,
    TensorboardLogger,
)
from avalanche.training.plugins import EvaluationPlugin
from avalanche.training.strategies import Naive

# 1. Parse command line arguments to determine the device to use for computations.
parser = argparse.ArgumentParser()
parser.add_argument("--device", default="cpu")
args = parser.parse_args()
device = torch.device(args.device)

# 2. Define transformations for training and testing data.
train_transform = transforms.Compose(
    [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
)
test_transform = transforms.Compose(
    [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
)

# 3. Create a benchmark using the MNIST dataset with the defined transformations.
benchmark = MNIST(
    scenario="ni",
    train_transform=train_transform,
    eval_transform=test_transform,
)

# 4. Create a simple MLP model with the number of classes equal to the number of classes in the benchmark.
model = SimpleMLP(num_classes=benchmark.n_classes)

# 5. Define various loggers including a text logger, an interactive logger, a CSV logger, and a Tensorboard logger.
loggers = [
    TextLogger(log_every=10),
    InteractiveLogger(),
    CSVLogger(),
    TensorboardLogger(),
]

# 6. Define an evaluation plugin that computes a wide range of metrics including accuracy, loss, class accuracy, AMCA, forgetting, backward transfer, forward transfer, CPU usage, timing, RAM usage, GPU usage, disk usage, MAC, and labels repartition metrics. The plugin should log these metrics using the defined loggers.
eval_plugin = EvaluationPlugin(
    accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    loss_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    forgetting_metrics(experience=True, stream=True),
    cpu_usage_metrics(experience=True),
    timing_metrics(epoch=True, experience=True),
    ram_usage_metrics(every=0.5, experience=True),
    disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True),
    MAC_metrics(experience=True),
    bwt_metrics(experience=True, stream=True),
    confusion_matrix_metrics(save_image=True, normalize='all', stream=True),
    labels_repartition_metrics(stream=True),
    loggers=loggers,
)

# 7. Create a Naive continual learning strategy using the defined model, an SGD optimizer, a CrossEntropyLoss loss function, and the defined evaluation plugin.
cl_strategy = Naive(
    model,
    SGD(model.parameters(), lr=0.001, momentum=0.9),
    CrossEntropyLoss(),
    train_mb_size=100,
    train_epochs=4,
    eval_mb_size=100,
    device=device,
    evaluator=eval_plugin,
)

# 8. Train the model on the benchmark's training stream and evaluate it on the benchmark's test stream, printing the results after each experience.
for experience in benchmark.train_stream:
    cl_strategy.train(experience)
    cl_strategy.eval(benchmark.test_stream)

# 9. After all experiences, print all the metrics stored by the evaluation plugin.
print("Final metrics: ", eval_plugin.get_last_metrics())
```

This code defines a simple MLP model, creates a benchmark using the MNIST dataset, and trains a Naive continual learning strategy using the model and benchmark. The evaluation plugin is used to compute various metrics during training and evaluation. The code also defines various loggers to log the metrics.