code: def download(self):
      import logging
  
      logger = logging.getLogger("OGBNPapers100MDataset")
      logger.info("Processing dataset...")
  
      dataset_path = os.path.join(self.__dataset_dir, "ogbn_papers100M")
  
      meta_json_path = os.path.join(dataset_path, "meta.json")
      if not os.path.exists(meta_json_path):
          j = {
              "num_nodes": {"paper": 111059956},
              "num_edges": {"paper__cites__paper": 1615685872},
          }
          with open(meta_json_path, "w") as file:
              json.dump(j, file)
  
      dataset = None
      if not os.path.exists(dataset_path):
          from ogb.nodeproppred import NodePropPredDataset
  
          dataset = NodePropPredDataset(
              name="ogbn-papers100M", root=self.__dataset_dir
          )
  
      features_path = os.path.join(dataset_path, "npy", "paper")
      os.makedirs(features_path, exist_ok=True)
  
      logger.info("Processing node features...")
      if self.__replication_factor == 1:
          replication_path = os.path.join(
              features_path, "node_feat.npy"
          )
      else:
          replication_path = os.path.join(
              features_path,
              f"node_feat_{self.__replication_factor}x.npy",
          )
      if not os.path.exists(replication_path):
          if dataset is None:
              from ogb.nodeproppred import NodePropPredDataset
  
              dataset = NodePropPredDataset(
                  name="ogbn-papers100M", root=self.__dataset_dir
              )
  
          node_feat = dataset[0][0]["node_feat"]
          if self.__replication_factor != 1:
              node_feat_replicated = np.concat(
                  [node_feat] * self.__replication_factor
              )
              node_feat = node_feat_replicated
          np.save(replication_path, node_feat)
  
      logger.info("Processing edge index...")
      edge_index_parquet_path = os.path.join(
          dataset_path, "parquet", "paper__cites__paper"
      )
      os.makedirs(edge_index_parquet_path, exist_ok=True)
  
      edge_index_parquet_file_path = os.path.join(
          edge_index_parquet_path, "edge_index.parquet"
      )
      if not os.path.exists(edge_index_parquet_file_path):
          if dataset is None:
              from ogb.nodeproppred import NodePropPredDataset
  
              dataset = NodePropPredDataset(
                  name="ogbn-papers100M", root=self.__dataset_dir
              )
  
          edge_index = dataset[0][0]["edge_index"]
          eidf = pandas.DataFrame(
              {"src": edge_index[0], "dst": edge_index[1]}
          )
          eidf.to_parquet(edge_index_parquet_file_path)
  
      edge_index_npy_path = os.path.join(
          dataset_path, "npy", "paper__cites__paper"
      )
      os.makedirs(edge_index_npy_path, exist_ok=True)
  
      edge_index_npy_file_path = os.path.join(
          edge_index_npy_path, "edge_index.npy"
      )
      if not os.path.exists(edge_index_npy_file_path):
          if dataset is None:
              from ogb.nodeproppred import NodePropPredDataset
  
              dataset = NodePropPredDataset(
                  name="ogbn-papers100M", root=self.__dataset_dir
              )
  
          edge_index = dataset[0][0]["edge_index"]
          np.save(edge_index_npy_file_path, edge_index)
  
      logger.info("Processing labels...")
      node_label_path = os.path.join(dataset_path, "parquet", "paper")
      os.makedirs(node_label_path, exist_ok=True)
  
      node_label_file_path = os.path.join(
          node_label_path, "node_label.parquet"
      )
      if not os.path.exists(node_label_file_path):
          if dataset is None:
              from ogb.nodeproppred import NodePropPredDataset
  
              dataset = NodePropPredDataset(
                  name="ogbn-papers100M", root=self.__dataset_dir
              )
  
          ldf = pandas.Series(dataset[0][1].T[0])
          ldf = (
              ldf[ldf >= 0]
              .reset_index()
              .rename(columns={"index": "node", 0: "label"})
          )
          ldf.to_parquet(node_label_file_path)
  
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 2 for calling the OGBNPapers100MDataset obj's underlying member method code with example usage: obj.download() without return types