code: def x_dict(self) -> Dict[str, torch.Tensor]:
      node_type_path = os.path.join(
          self.__dataset_dir, "ogbn_papers100M", "npy", "paper"
      )
  
      if self.__disk_x is None:
          if self.__replication_factor == 1:
              full_path = os.path.join(node_type_path, "node_feat.npy")
          else:
              full_path = os.path.join(
                  node_type_path,
                  f"node_feat_{self.__replication_factor}x.npy",
              )
  
          self.__disk_x = {"paper": np.load(full_path, mmap_mode="r")}
  
      return self.__disk_x
  
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 4 for calling the OGBNPapers100MDataset obj's underlying member method code with example usage: obj.x_dict() and returns: self