docstr: Office-Caltech-10 Dataset. This dataset consists of four domains: 'amazon', 'caltech', 'dslr', and 'webcam',
      which are samples with overlapped 10 classes between Office-31 and Caltech-256.
  
  Args:
      root (string): path to directory where the office folder will be created (or exists).
  
  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 (Unpublished).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).
docstr_tok: ["Office-Caltech-10","Dataset",".","This","dataset","consists","of","four","domains",":","'amazon","'",",","'caltech","'",",","'dslr","'",",","and","'webcam","'",",","which","are","samples","with","overlapped","10","classes","between","Office-31","and","Caltech-256",".","Args",":","root","(","string",")",":","path","to","directory","where","the","office","folder","will","be","created","(","or","exists",")",".","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Unpublished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",")","."]
code: def __init__(self, root, **kwargs):
      """Office-Caltech-10 Dataset. This dataset consists of four domains: 'amazon', 'caltech', 'dslr', and 'webcam',
          which are samples with overlapped 10 classes between Office-31 and Caltech-256.
  
      Args:
          root (string): path to directory where the office folder will be created (or exists).
  
      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).
      """
      sub_class_set = [
          "mouse",
          "calculator",
          "back_pack",
          "keyboard",
          "monitor",
          "projector",
          "headphones",
          "bike",
          "laptop_computer",
          "mug",
      ]
      super(OfficeCaltech, self).__init__(
          root, sub_class_set=sub_class_set, **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 OfficeCaltech object from inherited base classes: OfficeAccess with example: obj = OfficeCaltech(root)