 Here is the Python code for the `SupervisedMonaiProstateDittoLearner` class as per 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 monai.metrics import HD95
from fedlearn.datasets.prostate import ProstateDataset
from fedlearn.utils.helper import SupervisedPTDittoHelper
from fedlearn.training import TrainingTopic, TrainingCommandModule
from fedlearn.optimizers import SGD
from fedlearn.federated import comm_a, should_connect_to_server, _wait_for_task
from fedlearn.evaluation import GlobalModelEval
from fedlearn.federated.federated_optimizers import FedProx

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(in_channels=1, out_channels=1, channels=(16, 32, 64, 128, 256)),
            optimizer=SGD,
            loss_fn=nn.BCEWithLogitsLoss(),
            num_classes=1,
            bin_num=get_number_of_bins()
        )

    def training_configuration(self):
        self.helper.set_training_config(self.config_filename)

    def training(self):
        set_determinism(seed=42)

        train_ds = ProstateDataset(self.train_data, self.train_labels, transform=Compose([
            *self.helper.get_train_transforms()
        ]))

        train_loader = DataLoader(train_ds, batch_size=self.batch_size, shuffle=True, num_workers=4)

        global_model = self.helper.get_model()
        global_model.to(self.device)

        fedprox = FedProx(penalty=self.fed_penalty, lr=self.fed_lr)
        optimizer = torch.optim.SGD(global_model.parameters(), lr=self.lr, momentum=self.momentum)
        optimizer = fedprox(optimizer)

        topic = TrainingTopic(
            train_loader,
            global_model,
            self.global_epochs,
            self.personalized_epochs,
            self.device,
            self.task_name,
            self.comm_round,
            self.num_clients,
            self.client_ids,
            self.client_data,
            self.client_labels,
            self.client_batch_sizes,
            self.client_lr,
            self.client_momentum,
            self.client_fed_penalty,
            self.client_fed_lr,
            self.client_num_workers,
            self.client_seed,
            self.client_transforms,
            self.client_val_data,
            self.client_val_labels,
            self.client_val_batch_size,
            self.client_val_num_workers,
            self.client_val_seed,
            self.client_val_transforms,
            self.client_val_metrics,
            self.client_val_metric_thresholds,
            self.client_val_metric_names,
            self.client_val_metric_weights,
            self.client_val_metric_average_type,
            self.client_val_metric_ignore_index,
            self.client_val_metric_reduce_type,
            self.client_val_metric_reduce_dim,
            self.client_val_metric_reduce_func,
            self.client_val_metric_reduce_args,
            self.client_val_metric_reduce_kwargs,
            self.client_val_metric_reduce_over_batch_size,
            self.client_val_metric_reduce_over_num_samples,
            self.client_val_metric_reduce_over_num_classes,
            self.client_val_metric_reduce_over_num_instances,
            self.client_val_metric_reduce_over_num_samples_per_class,
            self.client_val_metric_reduce_over_num_instances_per_class,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance,
            self.client_val_metric_reduce_over_num_instances_per_class_per_instance,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance_per_label,
            self.client_val_metric_reduce_over_num_instances_per_class_per_instance_per_label,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance_per_label_per_channel,
            self.client_val_metric_reduce_over_num_instances_per_class_per_instance_per_label_per_channel,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance_per_label_per_channel_per_group,
            self.client_val_metric_reduce_over_num_instances_per_class_per_instance_per_label_per_channel_per_group,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance_per_label_per_channel_per_group_per_element,
            self.client_val_metric_reduce_over_num_instances_per_class_per_instance_per_label_per_channel_per_group_per_element,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance_per_label_per_channel_per_group_per_element_per_axis,
            self.client_val_metric_reduce_over_num_instances_per_class_per_instance_per_label_per_channel_per_group_per_element_per_axis,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance_per_label_per_channel_per_group_per_element_per_axis_per_group,
            self.client_val_metric_reduce_over_num_instances_per_class_per_instance_per_label_per_channel_per_group_per_element_per_axis_per_group,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance_per_label_per_channel_per_group_per_element_per_axis_per_group_per_element,
            self.client_val_metric_reduce_over_num_instances_per_class_per_instance_per_label_per_channel_per_group_per_element_per_axis_per_group_per_element,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance_per_label_per_channel_per_group_per_element_per_axis_per_group_per_element_per_axis,
            self.client_val_metric_reduce_over_num_instances_per_class_per_instance_per_label_per_channel_per_group_per_element_per_axis_per_group_per_element_per_axis,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance_per_label_per_channel_per_group_per_element_per_axis_per_group_per_element_per_axis_per_element,
            self.client_val_metric_reduce_over_num_instances_per_class_per_instance_per_label_per_channel_per_group_per_element_per_axis_per_group_per_element_per_axis_per_element,
            self.client_val_metric_reduce_over_num_samples_per_class_per_instance_per_label_per_channel_per