class_docstr: A class for saving graphs and split in OGB-compatible manner
  Create submission_datasetname/ directory, and output the following two files:
      - datasetname.zip (OGB-compatible zipped dataset folder)
      - meta_dict.pt (torch files storing all the necessary dataset meta-information)
class_docstr_tok: ["A","class","for","saving","graphs","and","split","in","OGB-compatible","manner","Create","submission_datasetname\/","directory",",","and","output","the","following","two","files",":","-","datasetname.zip","(","OGB-compatible","zipped","dataset","folder",")","-","meta_dict.pt","(","torch","files","storing","all","the","necessary","dataset","meta-information",")"]
code: def copy_mapping_dir(self, mapping_dir):
      target_mapping_dir = osp.join(self.dataset_dir, "mapping")
      os.makedirs(target_mapping_dir, exist_ok=True)
      file_list = [
          f
          for f in os.listdir(mapping_dir)
          if osp.isfile(osp.join(mapping_dir, f))
      ]
      if "README.md" not in file_list:
          raise RuntimeError(
              f"README.md must be included in mapping_dir {mapping_dir}"
          )
  
      # copy all the files in the mapping_dir to
      for f in file_list:
          shutil.copyfile(
              osp.join(mapping_dir, f), osp.join(target_mapping_dir, f)
          )
  
      self._copy_mapping_dir_done = True
  
imports: import torch
  import pandas
  import os
  import os.path
  from datetime import date
  import shutil
  from tqdm.auto import tqdm
  import numpy
  from ogb.io.read_graph_raw import read_binary_graph_raw, read_binary_heterograph_raw
  from ogb.utils.torch_util import all_numpy
label_desc: Use image node_id 8 for calling the DatasetSaver obj's underlying member method code with example usage: obj.copy_mapping_dir(mapping_dir) without return types