class_docstr: The class controlling how the multiple domains are iterated over.
  
  Args:
      data_access (MultiDomainImageFolder, or MultiDomainAccess): Multi-domain data access.
      valid_split_ratio (float, optional): Split ratio for validation set. Defaults to 0.1.
      test_split_ratio (float, optional): Split ratio for test set. Defaults to 0.2.
      random_state (int, optional): Random state for generator. Defaults to 1.
      test_on_all (bool, optional): Whether test model on all target. Defaults to False.
class_docstr_tok: ["The","class","controlling","how","the","multiple","domains","are","iterated","over",".","Args",":","data_access","(","MultiDomainImageFolder",",","or","MultiDomainAccess",")",":","Multi-domain","data","access",".","valid_split_ratio","(","float",",","optional",")",":","Split","ratio","for","validation","set",".","Defaults","to","0.1.","test_split_ratio","(","float",",","optional",")",":","Split","ratio","for","test","set",".","Defaults","to","0.2.","random_state","(","int",",","optional",")",":","Random","state","for","generator",".","Defaults","to","1.","test_on_all","(","bool",",","optional",")",":","Whether","test","model","on","all","target",".","Defaults","to","False","."]
code: def __len__(self):
      return len(self.data_access)
  
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 4 for calling the MultiDomainAdapDataset obj's underlying member method code with example usage: obj.__len__() and returns: len