code: def _torch_sparse_mm(torch_A1, torch_A2):
      with warnings.catch_warnings():
          warnings.simplefilter("ignore", category=UserWarning)
          return torch.sparse.mm(torch_A1, torch_A2)
  
file_path: dgl/tests/python/pytorch/sparse/test_matmul.py
imports: import warnings
  import backend
  import pytest
  import torch
  from dgl.sparse import bspmm, diag, from_coo, val_like
  from dgl.sparse.matmul import matmul
  from .utils import clone_detach_and_grad, dense_mask, rand_coo, rand_csc, rand_csr, rand_stride, sparse_matrix_to_dense, sparse_matrix_to_torch_sparse
label_desc: Use image node_id 1 for calling a global function with example usage: _torch_sparse_mm(torch_A1, torch_A2) and returns: torch