class_docstr: The Synthetic datasets used in
  `Parameterized Explainer for Graph Neural Network <https://arxiv.org/abs/2011.04573>`_.
  It takes Barabási–Albert(BA) graph or balance tree as base graph
  and randomly attachs specific motifs to the base graph.
  
  Args:
      root (:obj:`str`): Root data directory to save datasets
      name (:obj:`str`): The name of the dataset. Including :obj:`BA_shapes`, BA_grid,
      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`)
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code: def download(self):
      url = self.url.format(self.names[self.name][1])
      path = download_url(url, self.raw_dir)
  
imports: import os
  import torch
  import pickle
  import numpy
  import os.path
  import tqdm
  from torch_geometric.utils import dense_to_sparse
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
  from torch_geometric.data.dataset import files_exist
  import shutil
label_desc: Use image node_id 6 for calling the SynGraphDataset obj's underlying member method code with example usage: obj.download() without return types