 Here is the Python code based on your instructions:

```python
import torch
from monai.data import DataLoader
from monai.networks.nets import UNet
from monai.metrics import HD95, DiceCoefficient
from monai.utils import set_determinism
from monai.transforms import Compose
from ditto.ditto_learner import SupervisedPTDittoHelper
from ditto.ditto_learner import SupervisedMonaiProstateLearner

class SupervisedMonaiProstateDittoLearner(SupervisedMonaiProstateLearner):
    def __init__(self, config_filename, aggregation_epochs, ditto_model_epochs, task_name):
        super().__init__(config_filename, task_name)
        self.aggregation_epochs = aggregation_epochs
        self.ditto_model_epochs = ditto_model_epochs
        self.helper = SupervisedPTDittoHelper(config_filename, aggregation_epochs, ditto_model_epochs, task_name)

    def train_config(self):
        model = UNet(config=self.config['model'])
        optimizer = torch.optim.Adam(model.parameters())
        self.helper.set_model(model, optimizer)

    def train(self):
        set_determinism(self.config['deterministic'])
        train_transform = Compose([...])  # Define your training transforms
        val_transform = Compose([...])  # Define your validation transforms

        train_dataloader = DataLoader(self.train_dataset, batch_size=self.config['batch_size'], num_workers=self.config['num_workers'], collate_fn=self.collate_fn, drop_last=True, shuffle=True, pin_memory=True, persistent_workers=True, transform=train_transform)
        val_dataloader = DataLoader(self.val_dataset, batch_size=self.config['batch_size'], num_workers=self.config['num_workers'], collate_fn=self.collate_fn, drop_last=False, shuffle=False, pin_memory=True, persistent_workers=True, transform=val_transform)

        metrics = [DiceCoefficient(num_classes=self.num_classes), HD95(num_classes=self.num_classes)]

        for epoch in range(self.config['epochs']):
            self.helper.train_one_epoch(train_dataloader, metrics, self.optimizer)
            self.helper.validate(val_dataloader, metrics)

            if self.helper.should_abort():
                break

            self.helper.aggregate_weights()
            self.helper.update_local_model()

            local_model = self.helper.get_local_model()
            personalized_model = self.helper.get_personalized_model()

            self.helper.train_local_model(local_model, personalized_model)
            self.helper.validate_local_model(local_model)

            delta_model = self.helper.compute_model_diff(local_model, personalized_model)

            shareable_object = self.helper.get_shareable_object(local_model)
            self.helper.submit_model(shareable_object)
```