class_docstr: Convert multiple digits-like data accesses to a single data access.
  Args:
      data_access (dict): Dictionary of data accesses, e.g. {"Domain1_name": domain1_access,
          "Domain2_name": domain2_access}
      n_classes (int): number of classes.
      return_domain_label (Optional[bool], optional): Whether return domain labels in each batch.
          Defaults to False.
class_docstr_tok: ["Convert","multiple","digits-like","data","accesses","to","a","single","data","access",".","Args",":","data_access","(","dict",")",":","Dictionary","of","data","accesses",",","e.g",".","{","``","Domain1_name","''",":","domain1_access",",","``","Domain2_name","''",":","domain2_access","}","n_classes","(","int",")",":","number","of","classes",".","return_domain_label","(","Optional","[","bool","]",",","optional",")",":","Whether","return","domain","labels","in","each","batch",".","Defaults","to","False","."]
code: def __len__(self):
      return len(self.get_train()) + len(self.get_test())
  
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 MultiDomainAccess obj's underlying member method code with example usage: obj.__len__() and returns: unknown