

```python
from monai.networks.nets import UNet
from torch.optim import Adam

class SupervisedMonaiProstateDittoLearner(SupervisedMonaiProstateLearner):
    def __init__(self, train_config_filename, num_aggregation_epochs, num_ditto_model_epochs, task_name):
        super().__init__(train_config_filename, task_name)
        self.num_aggregation_epochs = num_aggregation_epochs
        self.num_ditto_model_epochs = num_ditto_model_epochs
        self.ditto_helper = SupervisedPTDittoHelper()

    def train_config(self, train_config):
        super().train_config(train_config)
        self.model = UNet(
            dimensions=3,
            in_channels=1,
            out_channels=1,
            channels=(16, 32, 64, 128, 256),
            strides=(2, 2, 2, 2),
            num_res_units=2,
        ).to(self.device)
        self.optimizer = Adam(self.model.parameters(), lr=self.train_config.learning_rate)
        self.ditto_helper.setup(self.model, self.optimizer)

    def train(self, abort_signal):
        if abort_signal.triggered:
            return make_reply(ReturnCode.TASK_ABORTED)
        self.ditto_helper.update_local_model_weights(self.model)
        self.ditto_helper.load_ditto_personalized_model()
        self.ditto_helper.local_train(
            self.model,
            self.train_config,
            self.dataloader,
            self.num_aggregation_epochs,
            self.num_ditto_model_epochs,
            self.device,
            self.optimizer,
            self.criterion,
            abort_signal,
        )
        self.ditto_helper.validate(self.model, self.train_config, self.dataloader, self.device)
        self.ditto_helper.compute_delta_model()
        return self.ditto_helper.get_shareable()
```

This code defines a class named `SupervisedMonaiProstateDittoLearner` which inherits from `SupervisedMonaiProstateLearner`. The initializer sets up the training configuration filename, number of aggregation epochs, number of ditto model epochs, and training task name. It also initializes a `SupervisedPTDittoHelper` instance.

The `train_config` method initializes the superclass, sets up a `UNet` model, and an `Adam` optimizer for the `SupervisedPTDittoHelper` instance.

The `train` method handles abort signals, updates local model weights with received weights, loads Ditto personalized model, performs local training on the reference model and personalized model, validates the Ditto model each round, computes the delta model, and returns a shareable object with the updated local model.