code: import torch
  
  from mp.agents.segmentation_semisup_domain_pred_agent import SegmentationSemisupDomainPredictionAgent
  from mp.data.pytorch.domain_prediction_dataset_wrapper import DomainPredictionDatasetWrapper
  from mp.eval.accumulator import Accumulator
  from mp.eval.inference.predict import softmax
  from mp.utils.domain_prediction_utils import perform_stage1_training_epoch
  from mp.utils.early_stopping import EarlyStopping
  from mp.utils.helper_functions import zip_longest_with_cycle
  
  
  class SegmentationSemisupDomainPredictionIRMAgent(SegmentationSemisupDomainPredictionAgent):
      r"""An Agent for segmentation models using a classifier for the domain space using the features from the encoder"""
  
      def perform_stage2_training_epoch(self, optimizer_model,
                                        optimizer_domain_predictor,
                                        optimizer_encoder,
                                        irm_loss_f_classifier,
                                        loss_f_domain_pred,
                                        loss_f_encoder,
                                        train_dataloaders_seg,
                                        train_dataloaders_dp,
                                        beta,
                                        print_run_loss=False):
          r"""Perform a stage 2 training epoch,
          meaning that the encoder, classifier and domain predictor are all trained one after the other
  
          Args:
              print_run_loss (bool): whether a running loss should be tracked and printed.
          """
          # The main difference in this semi-sup version is that
          # the domain predictor is trained using another set of dataloaders
  
          acc = Accumulator('loss')
          # We zip the dataloaders for segmentor and domain predictor
          # Each of these lists of dataloaders contains a dataloader per dataset
          for data_list_seg, data_list_dp in zip(zip_longest_with_cycle(*train_dataloaders_seg),
                                                 zip_longest_with_cycle(*train_dataloaders_dp)):
              classifier_losses = []
              classifier_penalties = []
  
              # For each dataloader
              for data_seg in data_list_seg:
                  # Get data for the segmentor
                  inputs, targets = self.get_inputs_targets(data_seg)
  
                  # Forward pass for the classification
                  # Here we cannot use self.get_outputs(inputs)
                  feature = self.model.get_features_from_encoder(inputs)
                  classifier_output = softmax(self.model.get_classification_from_features(feature))
  
                  # Store losses and predictions
                  classifier_losses.append(irm_loss_f_classifier.erm(classifier_output, targets))
                  classifier_penalties.append(irm_loss_f_classifier(classifier_output, targets))
  
              # Model Optimization step
              optimizer_model.zero_grad()
  
              loss = irm_loss_f_classifier.finalize_loss(classifier_losses, classifier_penalties)
              acc.add('loss', float(loss.detach().cpu()))
  
              loss.backward(retain_graph=True)
              optimizer_model.step()
  
              # Domain Predictor Optimization step
              data_lengths = []  # Is used to produce the domain targets on the fly
              features = []
              # For each dataloader
              for data_dp in data_list_dp:
                  # Get data
                  inputs, _ = self.get_inputs_targets(data_dp)
                  features.append(self.model.get_features_from_encoder(inputs))
                  data_lengths.append(inputs.shape[0])
  
              optimizer_domain_predictor.zero_grad()
              features = torch.cat(features, dim=0)
              domain_pred = self.model.get_domain_prediction_from_features(features.detach())
  
              domain_targets = self._create_domain_targets(data_lengths)
  
              loss_dm = loss_f_domain_pred(domain_pred, domain_targets)
              loss_dm.backward(retain_graph=False)
              optimizer_domain_predictor.step()
  
              # Encoder Optimization step based on domain prediction loss
              features = []
              for data_dp in data_list_dp:
                  # Get data
                  inputs, _ = self.get_inputs_targets(data_dp)
                  feature = self.model.get_features_from_encoder(inputs)
                  features.append(feature)
              features = torch.cat(features, dim=0)
  
              optimizer_encoder.zero_grad()
              domain_pred = self.model.get_domain_prediction_from_features(features)
              loss_encoder = beta * loss_f_encoder(domain_pred, domain_targets)
              loss_encoder.backward(retain_graph=False)
              optimizer_encoder.step()
  
          if print_run_loss:
              print('\nRunning loss: {}'.format(acc.mean('loss')))
  
apis: torch.cat