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):
          with np.errstate(**op.err):
              if op.is_gpu():
                  ret = cls._execute_gpu(op, xp, inputs[0])
              else:
                  ret = cls._execute_cpu(op, xp, inputs[0])
  
              for output, ret_element in zip(op.outputs, ret):
                  ctx[output.key] = ret_element
  
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 TensorTupleOp obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types