code: def _sparse_mm(context, node):
      inputs = _get_inputs(context, node, expected=2)
      x = mb.custom_torch_sparse_matmul(
          x=inputs[0],
          y=inputs[1],
          x_is_sparse=True,
          y_is_sparse=True,
          name=node.name,
      )
      context.add(x)
  
file_path: coremltools/coremltools/converters/mil/frontend/torch/test/test_custom_ops.py
imports: import pytest
  import torch
  import torch.nn
  from coremltools.converters.mil.frontend.torch.ops import _get_inputs
  from coremltools.converters.mil.frontend.torch.ops import cosine_similarity
  from coremltools.converters.mil.frontend.torch.torch_op_registry import _TORCH_OPS_REGISTRY
  from coremltools.converters.mil.frontend.torch.torch_op_registry import register_torch_op
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Operation, types
  from coremltools.converters.mil.mil.input_type import DefaultInputs, InputSpec, TensorInputType
  from coremltools.converters.mil.mil.ops.defs._op_reqs import register_op
  from .testing_utils import TorchBaseTest, convert_to_mlmodel
label_desc: Use image node_id 1 for calling the TestCustomOp obj's underlying member method code with example usage: obj._sparse_mm(context, node) without return types