code: def correctness(dataset):
      group, name = dataset
      mat_scipy = loadmat(f"{name}.mat")["Problem"][0][0][2].tocsr()
      row = torch.from_numpy(mat_scipy.tocoo().row).to(
          args.device, torch.long
      )
      col = torch.from_numpy(mat_scipy.tocoo().col).to(
          args.device, torch.long
      )
      mat = SparseTensor(row=row, col=col, sparse_sizes=mat_scipy.shape)
      mat.fill_cache_()
      mat_pytorch = mat.to_torch_sparse_coo_tensor().coalesce()
  
      for size in sizes:
          try:
              x = torch.randn((mat.size(1), size), device=args.device)
  
              out1 = mat @ x
              out2 = mat_pytorch @ x
  
              assert torch.allclose(out1, out2, atol=1e-4)
  
          except RuntimeError as e:
              if "out of memory" not in str(e):
                  raise RuntimeError(e)
              torch.cuda.empty_cache()
  
file_path: pytorch_sparse/benchmark/main.py
imports: import argparse
  import itertools
  import os.path
  import time
  import torch
  import wget
  from scipy.io import loadmat
  from torch_scatter import scatter_add
  from torch_sparse.tensor import SparseTensor
label_desc: Use image node_id 3 for calling a global function with example usage: correctness(dataset) without return types