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 zip(self):
      # check everything is done before zipping
      if not self._save_graph_list_done:
          raise RuntimeError("save_graph_list not completed.")
      if not self._save_split_done:
          raise RuntimeError("save_split not completed.")
      if not self._copy_mapping_dir_done:
          raise RuntimeError("copy_mapping_dir not completed.")
      if not self._save_target_labels_done:
          raise RuntimeError("save_target_labels not completed.")
      if not self._save_task_info_done:
          raise RuntimeError("save_task_info not completed.")
      if not self._get_meta_dict_done:
          raise RuntimeError("get_meta_dict not completed.")
  
      shutil.make_archive(self.dataset_dir, "zip", self.dataset_dir)
      self._zip_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 10 for calling the DatasetSaver obj's underlying member method code with example usage: obj.zip() without return types