code: def __len__(self):
      source_ds = self._source_by_split["train"]
      target_ds = self._target_by_split["train"]
      if self._labeled_target_by_split is None:
          return DatasetSizeType.get_size(
              self._size_type, source_ds, target_ds
          )
      else:
          labeled_target_ds = self._labeled_target_by_split["train"]
          return DatasetSizeType.get_size(
              self._size_type, source_ds, labeled_target_ds, target_ds
          )
  
imports: import logging
  import os
  from enum import Enum
  from typing import Any, Callable, cast, Dict, List, Optional, Tuple
  import numpy
  import torch.utils.data
  from sklearn.utils import check_random_state
  from torchvision.datasets import VisionDataset
  from torchvision.datasets.folder import default_loader, has_file_allowed_extension, IMG_EXTENSIONS
  from kale.loaddata.dataset_access import DatasetAccess, get_class_subset, split_by_ratios
  from kale.loaddata.sampler import FixedSeedSamplingConfig, get_labels, MultiDataLoader, SamplingConfig
label_desc: Use image node_id 5 for calling the MultiDomainDatasets obj's underlying member method code with example usage: obj.__len__() and returns: DatasetSizeType, DatasetSizeType