class_docstr: Common API for office dataset access
  
  Args:
      root (string): root directory of dataset
      transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed
          version. Defaults to office_transform.
      download (bool, optional): Whether to allow downloading the data if not found on disk. Defaults to False.
  
  References:
      [1] Saenko, K., Kulis, B., Fritz, M. and Darrell, T., 2010, September. Adapting visual category models to
      new domains. In European Conference on Computer Vision (pp. 213-226). Springer, Berlin, Heidelberg.
      [2] Griffin, Gregory and Holub, Alex and Perona, Pietro, 2007. Caltech-256 Object Category Dataset.
      California Institute of Technology. (Unpublished).
      https://resolver.caltech.edu/CaltechAUTHORS:CNS-TR-2007-001.
      [3] Gong, B., Shi, Y., Sha, F. and Grauman, K., 2012, June. Geodesic flow kernel for unsupervised
      domain adaptation. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 2066-2073).
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code: def __init__(
      self, root, transform=office_transform, download=False, **kwargs
  ):
      if download:
          self.download(root)
      super(OfficeAccess, self).__init__(
          root, transform=transform, **kwargs
      )
  
imports: import glob
  import logging
  import os
  from enum import Enum
  import numpy
  import pydicom
  import torch
  from torchvision import datasets, transforms
  from kale.loaddata.dataset_access import DatasetAccess
  from kale.loaddata.mnistm import MNISTM
  from kale.loaddata.multi_domain import MultiDomainAccess, MultiDomainImageFolder
  from kale.loaddata.usps import USPS
  from kale.prepdata.image_transform import get_transform
  from kale.utils.download import download_file_by_url
label_desc: Use image node_id 1 to create a new OfficeAccess object from inherited base classes: MultiDomainImageFolder, DatasetAccess with example: obj = OfficeAccess(root, transform, download)