code: def process(self):
      ### read pyg graph list
      add_inverse_edge = self.meta_info["add_inverse_edge"] == "True"
  
      if self.meta_info["additional node files"] == "None":
          additional_node_files = []
      else:
          additional_node_files = self.meta_info[
              "additional node files"
          ].split(",")
  
      if self.meta_info["additional edge files"] == "None":
          additional_edge_files = []
      else:
          additional_edge_files = self.meta_info[
              "additional edge files"
          ].split(",")
  
      data_list = read_graph_pyg(
          self.raw_dir,
          add_inverse_edge=add_inverse_edge,
          additional_node_files=additional_node_files,
          additional_edge_files=additional_edge_files,
          binary=self.binary,
      )
  
      if self.task_type == "subtoken prediction":
          graph_label_notparsed = pd.read_csv(
              osp.join(self.raw_dir, "graph-label.csv.gz"),
              compression="gzip",
              header=None,
          ).values
          graph_label = [
              str(graph_label_notparsed[i][0]).split(" ")
              for i in range(len(graph_label_notparsed))
          ]
  
          for i, g in enumerate(data_list):
              g.y = graph_label[i]
  
      else:
          if self.binary:
              graph_label = np.load(
                  osp.join(self.raw_dir, "graph-label.npz")
              )["graph_label"]
          else:
              graph_label = pd.read_csv(
                  osp.join(self.raw_dir, "graph-label.csv.gz"),
                  compression="gzip",
                  header=None,
              ).values
  
          has_nan = np.isnan(graph_label).any()
  
          for i, g in enumerate(data_list):
              if "classification" in self.task_type:
                  if has_nan:
                      g.y = (
                          torch.from_numpy(graph_label[i])
                          .view(1, -1)
                          .to(torch.float32)
                      )
                  else:
                      g.y = (
                          torch.from_numpy(graph_label[i])
                          .view(1, -1)
                          .to(torch.long)
                      )
              else:
                  g.y = (
                      torch.from_numpy(graph_label[i])
                      .view(1, -1)
                      .to(torch.float32)
                  )
  
      if self.pre_transform is not None:
          data_list = [self.pre_transform(data) for data in data_list]
  
      data, slices = self.collate(data_list)
  
      print("Saving...")
      torch.save((data, slices), 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 7 for calling the PygGraphPropPredDataset obj's underlying member method code with example usage: obj.process() without return types