class_docstr: A suite of sparse matrix benchmarks known as the `Suite Sparse Matrix
  Collection <https://sparse.tamu.edu>`_ collected from a wide range of
  applications.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      group (str): The group of the sparse matrix.
      name (str): The name of the sparse matrix.
      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`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
code: def process(self) -> None:
      from scipy.io import loadmat
  
      with fsspec.open(self.raw_paths[0], "rb") as f:
          mat = loadmat(f)["Problem"][0][0][2].tocsr().tocoo()
  
      row = torch.from_numpy(mat.row).to(torch.long)
      col = torch.from_numpy(mat.col).to(torch.long)
      edge_index = torch.stack([row, col], dim=0)
  
      value = torch.from_numpy(mat.data).to(torch.float)
      edge_attr = None if torch.all(value == 1.0) else value
  
      size: Optional[torch.Size] = torch.Size(mat.shape)
      if mat.shape[0] == mat.shape[1]:
          size = None
  
      num_nodes = mat.shape[0]
  
      data = Data(
          edge_index=edge_index,
          edge_attr=edge_attr,
          size=size,
          num_nodes=num_nodes,
      )
  
      if self.pre_transform is not None:
          data = self.pre_transform(data)
  
      self.save([data], self.processed_paths[0])
  
file_path: pytorch_geometric/torch_geometric/datasets/suite_sparse.py
imports: import os.path
  from typing import Callable, Optional
  import fsspec
  import torch
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.io import fs
label_desc: Use image node_id 7 for calling the SuiteSparseMatrixCollection obj's underlying member method code with example usage: obj.process() without return types