 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

class SupervisedMonaiProstateDittoLearner(SupervisedMonaiProstateLearner):
    def __init__(self, config_filename, aggregation_epochs, ditto_model_epochs, task_name):
        super().__init__()
        self.config_filename = config_filename
        self.aggregation_epochs = aggregation_epochs
        self.ditto_model_epochs = ditto_model_epochs
        self.task_name = task_name
        self.helper = SupervisedPTDittoHelper(config_filename, task_name)
        self.model = UNet(config=self.helper.config)
        self.optimizer = torch.optim.Adam(self.model.parameters())

    def train_config(self):
        self.helper.setup(self.model, self.optimizer)

    def train(self, train_dataloader, val_dataloader):
        set_determinism(self.helper.random_seed)
        train_transform = Compose([self.helper.train_transform])
        val_transform = Compose([self.helper.val_transform])

        train_loader = DataLoader(train_dataloader, collate_fn=self.helper.collate_fn, num_workers=self.num_workers)
        val_loader = DataLoader(val_dataloader, collate_fn=self.helper.collate_fn, num_workers=self.num_workers)

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

        for epoch in range(self.ditto_model_epochs):
            self.model.train()
            for batch_idx, batch in enumerate(train_loader):
                # Training loop code here

            self.model.eval()
            with torch.no_grad():
                for batch_idx, batch in enumerate(val_loader):
                    # Validation loop code here

            # Update local model weights with received weights
            # Validation of Ditto model each round
            # Compute the delta model
            # Return a shareable object with the updated local model
```

Please note that this is a simplified version of the code and assumes that you have defined the necessary methods for the training loop and validation loop, as well as the methods for handling abort signals, updating local model weights, loading Ditto personalized models, and computing the delta model. Also, the import statements at the beginning of the code are not included because they depend on the specific setup of your project.