class_docstr: The `Organic Materials Database (OMDB)
  <https://omdb.mathub.io/dataset>`__ of bulk organic crystals.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      train (bool, optional): If :obj:`True`, loads the training dataset,
          otherwise the test dataset. (default: :obj:`True`)
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` 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
          an :obj:`torch_geometric.data.Data` 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 an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in the
          final dataset. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
class_docstr_tok: ["The","`","Organic","Materials","Database","(","OMDB",")","<","https",":","\/\/omdb.mathub.io\/dataset",">","`","__","of","bulk","organic","crystals",".","Args",":","root","(","str",")",":","Root","directory","where","the","dataset","should","be","saved",".","train","(","bool",",","optional",")",":","If",":","obj",":","`","True","`",",","loads","the","training","dataset",",","otherwise","the","test","dataset",".","(","default",":",":","obj",":","`","True","`",")","transform","(","callable",",","optional",")",":","A","function\/transform","that","takes","in","an",":","obj",":","`","torch_geometric.data.Data","`","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","an",":","obj",":","`","torch_geometric.data.Data","`","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","an",":","obj",":","`","torch_geometric.data.Data","`","object","and","returns","a","boolean","value",",","indicating","whether","the","data","object","should","be","included","in","the","final","dataset",".","(","default",":",":","obj",":","`","None","`",")","force_reload","(","bool",",","optional",")",":","Whether","to","re-process","the","dataset",".","(","default",":",":","obj",":","`","False","`",")"]
code: def __init__(
      self,
      root: str,
      train: bool = True,
      transform: Optional[Callable] = None,
      pre_transform: Optional[Callable] = None,
      pre_filter: Optional[Callable] = None,
      force_reload: bool = False,
  ) -> None:
      super().__init__(
          root,
          transform,
          pre_transform,
          pre_filter,
          force_reload=force_reload,
      )
      path = (
          self.processed_paths[0] if train else self.processed_paths[1]
      )
      self.load(path)
  
file_path: pytorch_geometric/torch_geometric/datasets/omdb.py
imports: import os.path
  from typing import Callable, List, Optional
  import numpy
  import torch
  from torch_geometric.data import Data, InMemoryDataset, extract_tar
label_desc: Use image node_id 1 to create a new OMDB object from inherited base classes: InMemoryDataset with example: obj = OMDB(root, train, transform, pre_transform, pre_filter, force_reload)