class MultiTaskModel(ResNet):
    def __init__(self):
        super(MultiTaskModel, self).__init__(BasicBlock, [2,2,2,2])
        resnet = torchvision.models.resnet34(pretrained=True)
        self.in_feature = resnet.fc.in_features
        self.tasks = []
        self.fc = None

        # add all layers that are not fc or classifier to the model
        self.shared = nn.Sequential()
        for name, module in resnet.named_children():
            if name != 'fc' and name != 'classifiers':
                self.shared.add_module(name, module)
        # self.classifiers.append(resnet.fc)

    
    def set_task(self, task):
        print("Setting task to", task)
        self.tasks.append(task)
        print(f"tasks are {self.tasks}")
        print(f"task index is {task_list.index(task)}")
        # add a new fc layer for the new task
        self.fc = nn.Linear(self.in_feature, classes_per_task[task_list.index(task)])
        self.fc.apply(kaiming_normal_init)
        print(f"fc is {self.fc}")
        
    def forward(self, x):
        x = self.shared(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

for task in dataset_names:
    train_loader, val_loader, _, _, _ = get_dataloaders(task, 0.8, batch_size)
    all_train_loaders[task] = train_loader
    all_val_loaders[task] = val_loader

for idx, task in tqdm.tqdm(enumerate(task_list)):
    current_train_loader = all_train_loaders[task]
    current_val_loader = all_val_loaders[task]
    model = MultiTaskModel().to(device)
    if idx > 0:
        print(f"Previous task: {task_list[idx-1]}")
        ckpt = torch.load(f"epoch_{task_list[idx-1]}.pth.tar")
        model.load_state_dict(ckpt['state_dict'])
        model = model.to(device)

    start_time = time.time()
    model, acc = fine_tune_EWC_acuumelation(current_train_loader, current_val_loader, model, reg_lambda=1, num_epochs=num_epochs, lr=0.008, batch_size=batch_size, weight_decay=0, current_task=task)
    ```

Here's a sample output : 

```bash
Model loaded for task svhn

Performance of previous task: flowers
fc set to Linear(in_features=512, out_features=103, bias=True)
Accuracy of the network on the 1311 test images: 3.75

Performance of previous task: scenes
fc set to Linear(in_features=512, out_features=67, bias=True)
Accuracy of the network on the 3123 test images: 5.013020833333333

Performance of previous task: birds
fc set to Linear(in_features=512, out_features=201, bias=True)
Accuracy of the network on the 2358 test images: 0.9982638888888888

Performance of previous task: cars
fc set to Linear(in_features=512, out_features=196, bias=True)
Accuracy of the network on the 1621 test images: 0.6875

Performance of previous task: aircraft
fc set to Linear(in_features=512, out_features=56, bias=True)
Accuracy of the network on the 2000 test images: 15.574596774193548

Performance of previous task: chars
fc set to Linear(in_features=512, out_features=63, bias=True)
Accuracy of the network on the 12599 test images: 43.247767857142854

Performance of previous task: svhn
fc set to Linear(in_features=512, out_features=10, bias=True)
Accuracy of the network on the 26032 test images: 96.13223522167488

Running accuracy for task svhn is [3.75, 5.013020833333333, 0.9982638888888888, 0.6875, 15.574596774193548, 43.247767857142854, 96.13223522167488]
Mean accuracy for task svhn is 23.629054939319072