code: def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
      func_name = f"{func.__module__}.{func.__name__}"
  
      res = cls._try_call_special_impl(func_name, args, kwargs)
      if res is not NotImplemented:
          return res
  
      # Otherwise, use a default implementation that construct dense
      # tensors and use that to compute values
      def unwrap(e):
          return (
              e.sparse_to_dense() if isinstance(e, SparseTensor) else e
          )
  
      # Wrap back all Tensors into our custom class
      def wrap(e):
          # Check for zeros and use that to get indices
          return (
              SparseTensor.from_dense(e)
              if isinstance(e, torch.Tensor)
              else e
          )
  
      rs = tree_map(
          wrap,
          func(
              *tree_map(unwrap, args), **tree_map(unwrap, kwargs or {})
          ),
      )
      return rs
  
imports: import torch
  from copy import deepcopy
  from torch.utils._pytree import tree_map
  from torch.testing._internal.logging_tensor import LoggingTensor
label_desc: Use image node_id 6 for calling the SparseTensor obj's underlying member method code with example usage: obj.__torch_dispatch__(cls, func, types, args, kwargs) and returns: rs, res, unknown, unknown