docstr: - name (str): name of the dataset
  - root (str): root directory to store the dataset folder
  - transform, pre_transform (optional): transform/pre-transform graph objects
  
  - meta_dict: dictionary that stores all the meta-information about data. Default is None, 
          but when something is passed, it uses its information. Useful for debugging for external contributers.
docstr_tok: ["-","name","(","str",")",":","name","of","the","dataset","-","root","(","str",")",":","root","directory","to","store","the","dataset","folder","-","transform",",","pre_transform","(","optional",")",":","transform\/pre-transform","graph","objects","-","meta_dict",":","dictionary","that","stores","all","the","meta-information","about","data",".","Default","is","None",",","but","when","something","is","passed",",","it","uses","its","information",".","Useful","for","debugging","for","external","contributers","."]
code: def __init__(
      self,
      name,
      root="dataset",
      transform=None,
      pre_transform=None,
      meta_dict=None,
  ):
      """
      - name (str): name of the dataset
      - root (str): root directory to store the dataset folder
      - transform, pre_transform (optional): transform/pre-transform graph objects
  
      - meta_dict: dictionary that stores all the meta-information about data. Default is None,
              but when something is passed, it uses its information. Useful for debugging for external contributers.
      """
  
      self.name = name  ## original name, e.g., ogbg-molhiv
  
      if meta_dict is None:
          self.dir_name = "_".join(name.split("-"))
  
          # check if previously-downloaded folder exists.
          # If so, use that one.
          if osp.exists(osp.join(root, self.dir_name + "_pyg")):
              self.dir_name = self.dir_name + "_pyg"
  
          self.original_root = root
          self.root = osp.join(root, self.dir_name)
  
          master = pd.read_csv(
              os.path.join(os.path.dirname(__file__), "master.csv"),
              index_col=0,
              keep_default_na=False,
          )
          if not self.name in master:
              error_mssg = "Invalid dataset name {}.\n".format(
                  self.name
              )
              error_mssg += "Available datasets are as follows:\n"
              error_mssg += "\n".join(master.keys())
              raise ValueError(error_mssg)
          self.meta_info = master[self.name]
  
      else:
          self.dir_name = meta_dict["dir_path"]
          self.original_root = ""
          self.root = meta_dict["dir_path"]
          self.meta_info = meta_dict
  
      # check version
      # First check whether the dataset has been already downloaded or not.
      # If so, check whether the dataset version is the newest or not.
      # If the dataset is not the newest version, notify this to the user.
      if osp.isdir(self.root) and (
          not osp.exists(
              osp.join(
                  self.root,
                  "RELEASE_v" + str(self.meta_info["version"]) + ".txt",
              )
          )
      ):
          print(self.name + " has been updated.")
          if (
              input("Will you update the dataset now? (y/N)\n").lower()
              == "y"
          ):
              shutil.rmtree(self.root)
  
      self.download_name = self.meta_info[
          "download_name"
      ]  ## name of downloaded file, e.g., tox21
  
      self.num_tasks = int(self.meta_info["num tasks"])
      self.eval_metric = self.meta_info["eval metric"]
      self.task_type = self.meta_info["task type"]
      self.__num_classes__ = int(self.meta_info["num classes"])
      self.binary = self.meta_info["binary"] == "True"
  
      super(PygGraphPropPredDataset, self).__init__(
          self.root, transform, pre_transform
      )
  
      self.data, self.slices = torch.load(self.processed_paths[0])
  
file_path: ogb/ogb/graphproppred/dataset_pyg.py
imports: import pandas
  import shutil, os
  import os.path
  import torch
  import numpy
  from torch_geometric.data import InMemoryDataset
  from ogb.utils.url import decide_download, download_url, extract_zip
  from ogb.io.read_graph_pyg import read_graph_pyg
label_desc: Use image node_id 1 to create a new PygGraphPropPredDataset object from inherited base classes: InMemoryDataset with example: obj = PygGraphPropPredDataset(name, root, transform, pre_transform, meta_dict)