code: def execute(cls, ctx, op: "KMeansLloydPostprocess"):
      (
          (centers_old, centers_new, center_shift, weight_in_clusters),
          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_center_shift = center_shift.copy()
          out_centers_new = centers_new.copy()
          update_center(
              centers_old,
              out_centers_new,
              out_center_shift,
              weight_in_clusters,
          )
  
          ctx[op.outputs[0].key] = out_centers_new
          ctx[op.outputs[1].key] = out_center_shift
  
imports: import numpy
  from sklearn.utils.extmath import row_norms
  from ...None import opcodes
  from ...core import OutputType, recursive_tile
  from ...core.operand import OperandStage
  from ...serialization.serializables import KeyField, BoolField, Int32Field
  from ...tensor.array_utils import as_same_device, device, 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_fast import update_center
  from ._k_means_lloyd import update_chunk_dense, update_chunk_sparse
label_desc: Use image node_id 11 for calling the KMeansLloydPostprocess obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types