class_docstr: Dataset base class for creating graph datasets.
  See `here <https://pytorch-geometric.readthedocs.io/en/latest/tutorial/
  create_dataset.html>`__ for the accompanying tutorial.
  
  Args:
      root (str, optional): Root directory where the dataset should be saved.
          (optional: :obj:`None`)
      transform (callable, optional): A function/transform that takes in a
          :class:`~torch_geometric.data.Data` or
          :class:`~torch_geometric.data.HeteroData` object and returns a
          transformed version.
          The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          a :class:`~torch_geometric.data.Data` or
          :class:`~torch_geometric.data.HeteroData` object and returns a
          transformed version.
          The data object will be transformed before being saved to disk.
          (default: :obj:`None`)
      pre_filter (callable, optional): A function that takes in a
          :class:`~torch_geometric.data.Data` or
          :class:`~torch_geometric.data.HeteroData` object and returns a
          boolean value, indicating whether the data object should be
          included in the final dataset. (default: :obj:`None`)
      log (bool, optional): Whether to print any console output while
          downloading and processing the dataset. (default: :obj:`True`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
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code: def __init__(
      self,
      root: Optional[str] = None,
      transform: Optional[Callable] = None,
      pre_transform: Optional[Callable] = None,
      pre_filter: Optional[Callable] = None,
      log: bool = True,
      force_reload: bool = False,
  ) -> None:
      super().__init__()
  
      if isinstance(root, str):
          root = osp.expanduser(fs.normpath(root))
  
      self.root = root or MISSING
      self.transform = transform
      self.pre_transform = pre_transform
      self.pre_filter = pre_filter
      self.log = log
      self._indices: Optional[Sequence] = None
      self.force_reload = force_reload
  
      if self.has_download:
          self._download()
  
      if self.has_process:
          self._process()
  
imports: import copy
  import os.path
  import re
  import sys
  import warnings
  from abc import ABC, abstractmethod
  from collections.abc import Sequence
  from typing import Any, Callable, Iterable, Iterator, List, Optional, Tuple, Union
  import numpy
  import torch.utils.data
  from torch import Tensor
  from torch_geometric.data.data import BaseData
  from torch_geometric.io import fs
label_desc: Use image node_id 7 to create a new Dataset object from inherited base classes: torch.utils.data, ABC with example: obj = Dataset(root, transform, pre_transform, pre_filter, log, force_reload)