class_docstr: The GOOD-Arxiv dataset adapted from `OGB
  <https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html>`_ benchmark.
  
  Args:
      root (str): The dataset saving root.
      domain (str): The domain selection. Allowed: 'degree' and 'time'.
      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-Arxiv","dataset","adapted","from","`","OGB","<","https",":","\/\/proceedings.neurips.cc\/paper\/2020\/hash\/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html",">","`","_","benchmark",".","Args",":","root","(","str",")",":","The","dataset","saving","root",".","domain","(","str",")",":","The","domain","selection",".","Allowed",":","'degree","'","and","'time","'",".","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 _download(self):
      if (
          os.path.exists(osp.join(self.raw_dir, self.name))
          or self.generate
      ):
          return
      if not os.path.exists(self.raw_dir):
          os.makedirs(self.raw_dir)
      self.download()
  
file_path: DIG/dig/oodgraph/good_arxiv.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 3 for calling the GOODArxiv obj's underlying member method code with example usage: obj._download() without return types