class_docstr: The GOOD-CBAS dataset. Modified from `BA-Shapes
  <https://proceedings.neurips.cc/paper/2019/hash/d80b7040b773199015de6d3b4293c8ff-Abstract.html>`_.
  
  Args:
      root (str): The dataset saving root.
      domain (str): The domain selection. Allowed: 'color'.
      shift (str): The distributional shift we pick. Allowed: 'no_shift', 'covariate', and 'concept'.
      generate (bool): The flag for regenerating dataset. True: regenerate. False: download.
class_docstr_tok: ["The","GOOD-CBAS","dataset",".","Modified","from","`","BA-Shapes","<","https",":","\/\/proceedings.neurips.cc\/paper\/2019\/hash\/d80b7040b773199015de6d3b4293c8ff-Abstract.html",">","`","_",".","Args",":","root","(","str",")",":","The","dataset","saving","root",".","domain","(","str",")",":","The","domain","selection",".","Allowed",":","'color","'",".","shift","(","str",")",":","The","distributional","shift","we","pick",".","Allowed",":","'no_shift","'",",","'covariate","'",",","and","'concept","'",".","generate","(","bool",")",":","The","flag","for","regenerating","dataset",".","True",":","regenerate",".","False",":","download","."]
code: def raw_dir(self):
      return osp.join(self.root)
  
file_path: DIG/dig/oodgraph/good_cbas.py
imports: import os
  import os.path
  import gdown
  import torch
  from munch import Munch
  from torch_geometric.data import InMemoryDataset, extract_zip
label_desc: Use image node_id 2 for calling the GOODCBAS obj's underlying member method code with example usage: obj.raw_dir() and returns: osp