 Here is the Python code based on your instructions:

```python
import torch
import torch.nn as nn
from monai.networks.nets import UNet
from monai.utils import set_determinism
from monai.data import DataLoader
from monai.transforms import Compose
from supervised_pt_ditto_helper import SupervisedPTDittoHelper

class SupervisedMonaiProstateDittoLearner(SupervisedMonaiProstateLearner):
    def __init__(self, config_filename, global_epochs, personalized_epochs, task_name):
        super().__init__(config_filename, global_epochs, personalized_epochs, task_name)
        self.helper = SupervisedPTDittoHelper(
            model=UNet(spatial_dims=3, in_channels=self.config['data_keys']['image_channel'], out_channels=1),
            optimizer=torch.optim.SGD,
            **self.config['optimizer']
        )

    def training_configuration(self):
        set_determinism(seed=self.config['seed'])
        train_transforms = Compose([
            # Add your data augmentation transforms here
        ])
        self.train_dataset = self.get_dataset(split='train', transform=train_transforms)
        self.train_dataloader = DataLoader(self.train_dataset, **self.config['dataloader'])

    def train(self):
        for round_num in range(self.global_epochs):
            global_model_weights = self.helper.get_global_model_weights()

            for round_personalized in range(self.personalized_epochs):
                personalized_model_info = self.get_personalized_model_info(round_num, round_personalized)
                self.helper.prepare_fedprox_loss(personalized_model_info)

                reference_model = self.helper.model.load_state_dict(global_model_weights)
                personalized_model = self.helper.model.load_state_dict(personalized_model_info['personalized_model_state_dict'])

                try:
                    for epoch in range(self.config['epochs_per_round']):
                        self.helper.train_epoch(self.train_dataloader, reference_model, personalized_model)

                    updated_weights = self.helper.get_updated_weights()
                    global_model_weights = updated_weights['global_model_state_dict']
                    self.helper.update_global_model_weights(global_model_weights)

                except KeyboardInterrupt:
                    print("Training interrupted. Saving current model weights.")
                    self.save_model(global_model_weights, round_num, round_personalized)
                    raise

                except Exception as e:
                    print(f"An error occurred during training: {e}")
                    self.save_model(global_model_weights, round_num, round_personalized)
                    raise

            self.save_model(global_model_weights, round_num)
```