code: def test_min_sparse_shape(self, dtype, device, backend):
      SparseSemiStructuredTensor._FORCE_CUTLASS = backend == "cutlass"
      config = _DTYPE_TO_SEMI_STRUCTURED_SPARSE_CONFIG[dtype]
      A = rand_sparse_semi_structured_mask(
          config.sparse_min_rows,
          config.sparse_min_cols,
          dtype=dtype,
          device=device,
      )
      A_sparse = to_sparse_semi_structured(A)
      B = torch.rand(
          (config.sparse_min_cols, config.dense_min_cols), device=device
      ).to(dtype)
      if dtype == torch.int8:
          dense_res = torch.mm(A.cpu(), B.cpu()).to(
              device, dtype=torch.int8
          )
          # int8 sparse matmul not supported for R/R -> R layout, so we transpose one of the arguments to get R/C -> R
          B_t = B.t().contiguous()
          sparse_res = torch.mm(A_sparse, B_t.t())
      else:
          dense_res = torch.mm(A, B)
          sparse_res = torch.mm(A_sparse, B)
      assert torch.allclose(sparse_res, dense_res, rtol=1e-3, atol=1e-3)
  
imports: import itertools
  import random
  import unittest
  import torch
  from torch import nn
  from torch.sparse.semi_structured import _DTYPE_TO_SEMI_STRUCTURED_SPARSE_CONFIG, SparseSemiStructuredTensor, to_sparse_semi_structured
  from torch.testing import make_tensor
  from torch.testing._internal.common_device_type import dtypes, instantiate_device_type_tests
  from torch.testing._internal.common_dtype import all_types_and_complex
  import torch._dynamo.test_case
  from torch.testing._internal.common_utils import parametrize, run_tests, subtest, TestCase, TEST_WITH_ROCM, IS_WINDOWS
  from torch.utils._triton import has_triton
label_desc: Use image node_id 12 for calling the TestSparseSemiStructured obj's underlying member method code with example usage: obj.test_min_sparse_shape(dtype, device, backend) without return types