code: def _execute_reduce(cls, ctx, op):
      (
          (center_shift, lower_bounds, upper_bounds, labels),
          device_id,
          xp,
      ) = as_same_device(
          [ctx[inp.key] for inp in op.inputs],
          op.device,
          ret_extra=True,
          copy_if_not_writeable=True,
      )
  
      with device(device_id):
          out_upper_bounds = upper_bounds.copy()
          out_lower_bounds = lower_bounds.copy()
          update_upper_lower_bounds(
              out_upper_bounds, out_lower_bounds, labels, center_shift
          )
          ctx[op.outputs[0].key] = out_upper_bounds
          ctx[op.outputs[1].key] = out_lower_bounds
  
imports: import numpy
  from ...None import opcodes
  from ...core import OutputType, recursive_tile
  from ...core.operand import OperandStage
  from ...serialization.serializables import KeyField, Int32Field, BoolField
  from ...tensor.array_utils import as_same_device, device, cp, sparse
  from ...tensor.core import TensorOrder
  from ...utils import has_unknown_shape
  from ..operands import LearnOperand, LearnOperandMixin
  from ._k_means_common import _execute_merge_update, _relocate_empty_clusters
  from ._k_means_elkan import init_bounds_dense, init_bounds_sparse, update_chunk_dense, update_chunk_sparse
  from ._k_means_fast import update_center, update_upper_lower_bounds
label_desc: Use image node_id 15 for calling the KMeansElkanPostprocess obj's underlying member method code with example usage: obj._execute_reduce(cls, ctx, op) without return types