class_docstr: The SentiGraph datasets from `Explainability in Graph Neural Networks: A Taxonomic Survey
  <https://arxiv.org/abs/2012.15445>`_.
  The datasets take pretrained BERT as node feature extractor
  and dependency tree as edges to transfer the text sentiment datasets into
  graph classification datasets.
  
  The dataset `Graph-SST2 <https://drive.google.com/file/d/1-PiLsjepzT8AboGMYLdVHmmXPpgR8eK1/view?usp=sharing>`_
  should be downloaded to the proper directory before running. All the three datasets Graph-SST2, Graph-SST5, and
  Graph-Twitter can be download in this
  `link <https://drive.google.com/drive/folders/1dt0aGMBvCEUYzaG00TYu1D03GPO7305z?usp=sharing>`_.
  
  Args:
      root (:obj:`str`): Root directory where the datasets are saved
      name (:obj:`str`): The name of the datasets.
      transform (:obj:`Callable`, :obj:`None`): A function/transform that takes in an
          :class:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (:obj:`Callable`, :obj:`None`):  A function/transform that takes in
          an :class:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
  
  .. note:: The default parameter of pre_transform is :func:`~undirected_graph`
      which transfers the directed graph in original data into undirected graph before
      being saved to disk.
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code: def processed_dir(self):
      return osp.join(self.root, self.name, "processed")
  
imports: import os
  import glob
  import json
  import torch
  import pickle
  import numpy
  import os.path
  from torch_geometric.data import Data, InMemoryDataset
  import traceback
label_desc: Use image node_id 3 for calling the SentiGraphDataset obj's underlying member method code with example usage: obj.processed_dir() and returns: osp