code: def _test_sparse_op(
      self, op_name, inplace, dtype1, dtype2, device, coalesced
  ):
      if dtype1.is_complex or dtype2.is_complex:
          return
  
      suffix = "_" if inplace else ""
      err = f"{'  coalesced' if coalesced else 'uncoalesced'} {op_name + suffix}({dtype1}, {dtype2})"
  
      def op(t1, t2, suf=None):
          suf = suffix if suf is None else suf
          return getattr(t1, op_name + suf)(t2)
  
      add_sub = op_name == "add" or op_name == "sub"
  
      (dense1, sparse1) = self._test_sparse_op_input_tensors(
          device, dtype1, coalesced
      )
      (dense2, sparse2) = self._test_sparse_op_input_tensors(
          device, dtype2, coalesced, op_name != "div"
      )
  
      common_dtype = torch.result_type(dense1, dense2)
      if self.device_type == "cpu" and common_dtype == torch.half:
          self.assertRaises(RuntimeError, lambda: op(s1, d2))
  
      # Skip inplace tests that would fail due to inability to cast to the output type.
      # Some of these would also raise errors due to not being a supported op.
      if inplace and not torch.can_cast(common_dtype, dtype1):
          self.assertRaises(RuntimeError, lambda: op(dense1, sparse2))
          self.assertRaises(RuntimeError, lambda: op(sparse1, sparse2))
          self.assertRaises(RuntimeError, lambda: op(sparse1, dense2))
          return
  
      expected = op(dense1.clone(), dense2)
      precision = self._get_precision(expected.dtype, coalesced)
      rtol = None if precision is None else 0
      test_tensors = [expected, dense1, sparse1, dense2, sparse2]
      e, d1, s1, d2, s2 = (
          [x.clone() for x in test_tensors] if inplace else test_tensors
      )
  
      # Test op(sparse, sparse)
      if op_name != "div":
          sparse = op(s1, s2)
          self.assertEqual(sparse.dtype, e.dtype)
          self.assertEqual(
              e, sparse.to_dense(), atol=precision, rtol=rtol, msg=err
          )
      else:
          # sparse division only supports division by a scalar
          self.assertRaises(RuntimeError, lambda: op(s1, s2).to_dense())
  
      # Test op(dense, sparse)
      if add_sub or op_name == "mul":
          if inplace:
              e, d1, s1, d2, s2 = (x.clone() for x in test_tensors)
          dense_sparse = op(d1, s2)
          dense_sparse = (
              dense_sparse.to_dense()
              if dense_sparse.is_sparse
              else dense_sparse
          )
          self.assertEqual(
              e, dense_sparse, atol=precision, rtol=rtol, msg=err
          )
      else:
          # sparse division only supports division by a scalar
          # mul: Didn't find kernel to dispatch to for operator 'aten::_nnz'
          self.assertRaises(RuntimeError, lambda: op(d1, s2))
  
      # Test op(sparse, dense) not supported for all ops but 'mul'.
      # add(sparse, dense) is not supported. Use add(dense, sparse) instead.
      # sparse division only supports division by a scalar
      if op_name != "mul":
          self.assertRaises(RuntimeError, lambda: op(s1, d2))
      else:
          # No type promotions for inplace operations, hence suf=''
          op(s1, d2, suf="")
  
      # Test op(sparse, scalar)
      if not add_sub and not (
          self.device_type == "cpu" and dtype1 == torch.half
      ):
          if inplace:
              e, d1, s1, d2, s2 = (x.clone() for x in test_tensors)
          scalar = d2.view(d2.numel())[0].item()
  
          sparse = op(s1, scalar)
          dense_scalar = op(d1, scalar)
          self.assertEqual(sparse.dtype, dense_scalar.dtype)
          self.assertEqual(
              dense_scalar,
              sparse.to_dense(),
              atol=precision,
              rtol=rtol,
              msg=err,
          )
      else:
          # add(sparse, dense) is not supported. Use add(dense, sparse) instead.
          # "mul_cpu" / "div_cpu" not implemented for 'Half'
          self.assertRaises(
              RuntimeError,
              lambda: op(s1, d2.view(d2.numel())[0].item()),
          )
  
imports: from functools import wraps
  import itertools
  import unittest
  import torch
  from torch.testing._internal.common_utils import TestCase, run_tests, load_tests, make_tensor, TEST_NUMPY, set_default_dtype, torch_to_numpy_dtype_dict, numpy_to_torch_dtype_dict, skipIfTorchDynamo
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyNativeDeviceTypes, dtypes, onlyCPU, expectedFailureMeta, skipMeta
  from torch.testing._internal.common_dtype import all_types_and_complex_and, get_all_math_dtypes, floating_types, get_all_dtypes, float_to_corresponding_complex_type_map
  import numpy
  import operator
label_desc: Use image node_id 37 for calling the TestTypePromotion obj's underlying member method code with example usage: obj._test_sparse_op(op_name, inplace, dtype1, dtype2, device, coalesced) and returns: getattr