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 __init__(
      self,
      root: str,
      domain: str,
      shift: str = "no_shift",
      transform=None,
      pre_transform=None,
      generate: bool = False,
  ):
      self.name = self.__class__.__name__
      self.domain = domain
      self.metric = "Accuracy"
      self.task = "Multi-label classification"
      self.url = "https://drive.google.com/file/d/1-Wq7PoHTAiLsos20bLlq_xNvrV5AHSWu/view?usp=sharing"
  
      self.generate = generate
  
      super().__init__(root, transform, pre_transform)
      if shift == "covariate":
          subset_pt = 1
      elif shift == "concept":
          subset_pt = 2
      else:
          subset_pt = 0
  
      self.data, self.slices = torch.load(
          self.processed_paths[subset_pt]
      )
  
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 1 to create a new GOODArxiv object from inherited base classes: InMemoryDataset with example: obj = GOODArxiv(root, domain, shift, transform, pre_transform, generate)