class_docstr: An on-disk task.
  
  An on-disk task is for ``OnDiskDataset``. It contains the metadata and the
  train/val/test sets.
class_docstr_tok: ["An","on-disk","task",".","An","on-disk","task","is","for","``","OnDiskDataset","``",".","It","contains","the","metadata","and","the","train\/val\/test","sets","."]
code: def __repr__(self) -> str:
      ret = "{Classname}({attributes})"
  
      attributes_str = ""
  
      attributes = get_attributes(self)
      attributes.reverse()
      for attribute in attributes:
          if attribute[0] == "_":
              continue
          value = getattr(self, attribute)
          attributes_str += f"{attribute}={value},\n"
      attributes_str = textwrap.indent(
          attributes_str, " " * len("OnDiskTask(")
      ).strip()
  
      return ret.format(
          Classname=self.__class__.__name__, attributes=attributes_str
      )
  
imports: import json
  import os
  import shutil
  import textwrap
  from copy import deepcopy
  from typing import Dict, List, Union
  import torch
  import yaml
  import dgl
  from ...base import dgl_warning
  from ...data.utils import download, extract_archive
  from ..base import etype_str_to_tuple
  from ..dataset import Dataset, Task
  from ..internal import calculate_dir_hash, check_dataset_change, copy_or_convert_data, get_attributes, read_data, read_edges
  from ..itemset import ItemSet, ItemSetDict
  from ..sampling_graph import SamplingGraph
  from .fused_csc_sampling_graph import from_dglgraph, FusedCSCSamplingGraph
  from .ondisk_metadata import OnDiskGraphTopology, OnDiskMetaData, OnDiskTaskData, OnDiskTVTSet
  from .torch_based_feature_store import TorchBasedFeatureStore
label_desc: Use image node_id 6 for calling the OnDiskTask obj's underlying member method code with example usage: obj.__repr__() and returns: ret