code: def execute(cls, ctx, op):
      inputs, device_id, xp = as_same_device(
          [ctx[c.key] for c in op.inputs],
          device=op.device,
          ret_extra=True,
      )
  
      with device(device_id):
          kw = {"casting": op.casting} if op.out is not None else {}
  
          inputs_iter = iter(inputs)
          args = [
              a if np.isscalar(a) else next(inputs_iter)
              for a in op.args
          ]
          if op.out is not None:
              kw["out"] = next(inputs_iter).copy()
  
          with np.errstate(**op.err):
              if op.is_gpu():
                  ret = cls._execute_gpu(op, xp, *args, **kw)
              else:
                  ret = cls._execute_cpu(op, xp, *args, **kw)
  
              if ret.dtype != op.dtype:
                  ret = ret.astype(op.dtype)
              ctx[op.outputs[0].key] = ret
  
imports: import scipy.special
  from ...core import ExecutableTuple
  from ...None import opcodes
  from ..datasource import tensor
  from ..arithmetic.core import TensorUnaryOp, TensorBinOp, TensorMultiOp
  from ..array_utils import np, cp, issparse, sparse, convert_order, as_same_device, device
label_desc: Use image node_id 3 for calling the TensorSpecialMultiOp obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types