code: def __get_labels(self):
      label_path = os.path.join(
          self.__dataset_dir,
          "ogbn_papers100M",
          "parquet",
          "paper",
          "node_label.parquet",
      )
  
      node_label = pandas.read_parquet(label_path)
  
      if self.__replication_factor > 1:
          orig_num_nodes = (
              self.num_nodes("paper") // self.__replication_factor
          )
          dfr = pandas.DataFrame(
              {
                  "node": pandas.concat(
                      [
                          node_label.node + (r * orig_num_nodes)
                          for r in range(1, self.__replication_factor)
                      ]
                  ),
                  "label": pandas.concat(
                      [
                          node_label.label
                          for r in range(1, self.__replication_factor)
                      ]
                  ),
              }
          )
          node_label = pandas.concat([node_label, dfr]).reset_index(
              drop=True
          )
  
      num_nodes = self.num_nodes("paper")
      node_label_tensor = torch.full(
          (num_nodes,), -1, dtype=torch.float32, device="cpu"
      )
      node_label_tensor[
          torch.as_tensor(node_label.node.values, device="cpu")
      ] = torch.as_tensor(node_label.label.values, device="cpu")
  
      self.__y = {"paper": node_label_tensor.contiguous()}
  
      train_ix, test_val_ix = train_test_split(
          torch.as_tensor(node_label.node.values),
          train_size=self.__train_split,
          random_state=num_nodes,
      )
      test_ix, val_ix = train_test_split(
          test_val_ix,
          test_size=self.__val_split,
          random_state=num_nodes,
      )
  
      train_tensor = torch.full(
          (num_nodes,), 0, dtype=torch.bool, device="cpu"
      )
      train_tensor[train_ix] = 1
      self.__train = {"paper": train_tensor}
  
      test_tensor = torch.full(
          (num_nodes,), 0, dtype=torch.bool, device="cpu"
      )
      test_tensor[test_ix] = 1
      self.__test = {"paper": test_tensor}
  
      val_tensor = torch.full(
          (num_nodes,), 0, dtype=torch.bool, device="cpu"
      )
      val_tensor[val_ix] = 1
      self.__val = {"paper": val_tensor}
  
imports: from .dataset import Dataset
  from typing import Dict, Tuple, Union
  import pandas
  import torch
  import numpy
  from sklearn.model_selection import train_test_split
  import gc
  import os
  import json
label_desc: Use image node_id 13 for calling the OGBNPapers100MDataset obj's underlying member method code with example usage: obj.__get_labels() without return types