code: def __init__(self, root: str = "dataset"):
      self.original_root = root
  
      self.folder = osp.join(root, "wikikg90m-v2")
      self.version = 1
  
      # Old url hosted at Stanford
      # md5sum: bfd6257134b7eb59e2edc0a4af21faa8
      # self.url = 'http://ogb-data.stanford.edu/data/lsc/wikikg90m-v2.zip'
      # New url hosted by DGL team at AWS--much faster to download
      self.url = "https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/wikikg90m-v2.zip"
  
      self.processed_dir = osp.join(self.folder, "processed")
  
      if osp.isdir(osp.join(self.folder, "mapping")):
          shutil.rmtree(osp.join(self.folder, "mapping"))
  
      if osp.isdir(self.folder) and (
          not osp.exists(
              osp.join(self.folder, f"RELEASE_v{self.version}.txt")
          )
      ):
          print("WikiKG90M-v2 dataset has been updated.")
          if input("Will you update the dataset now? (y/N)\n") == "y":
              shutil.rmtree(osp.join(self.folder))
  
      self.download()
      self.__meta__ = torch.load(osp.join(self.folder, "meta.pt"))
  
      # training triplet
      path = osp.join(self.processed_dir, "train_hrt.npy")
      self._train_hrt = np.load(path)
  
      # node/edge features
      self._entity_feat = None
      self._all_entity_feat = None
      self._relation_feat = None
  
      # Validation
      self._valid_dict = None
  
      # Test
      self._test_dev_dict = None
      self._test_challenge_dict = None
  
imports: from typing import Optional, Union, Dict
  import os
  import shutil
  import os.path
  import torch
  import numpy
  import pandas
  from ogb.utils.url import decide_download, download_url, extract_zip, makedirs
label_desc: Use image node_id 1 to create a new WikiKG90Mv2Dataset object from inherited base classes: object with example: obj = WikiKG90Mv2Dataset(root)