code: def tile(cls, op: "KMeansLloydUpdate"):
      if has_unknown_shape(*op.inputs):
          yield
      x = op.x
      if x.chunk_shape[1] != 1:  # pragma: no cover
          x = yield from recursive_tile(x.rechunk({1: x.shape[1]}))
      sample_weight = yield from recursive_tile(
          op.sample_weight.rechunk({0: x.nsplits[0]})
      )
      x_squared_norms = yield from recursive_tile(
          op.x_squared_norms.rechunk({0: x.nsplits[0]})
      )
      labels = yield from recursive_tile(
          op.labels.rechunk({0: x.nsplits[0]})
      )
      assert len(op.centers_old.chunks) == 1
  
      labels_chunks, centers_new_chunks, weight_in_clusters_chunks = (
          [],
          [],
          [],
      )
      for i in range(x.chunk_shape[0]):
          x_chunk = x.cix[i, 0]
          sample_weight_chunk = sample_weight.cix[i,]
          x_squared_norms_chunk = x_squared_norms.cix[i,]
          labels_chunk = labels.cix[i,]
          chunk_op = op.copy().reset_key()
          chunk_op.stage = OperandStage.map
          chunk_kws = [
              labels_chunk.params,
              {
                  "index": (0, 0),
                  "shape": (op.n_clusters, x_chunk.shape[1]),
                  "dtype": op.centers_old.dtype,
                  "order": TensorOrder.C_ORDER,
              },
              {
                  "index": (0,),
                  "shape": (op.n_clusters,),
                  "dtype": op.centers_old.dtype,
                  "order": TensorOrder.C_ORDER,
              },
          ]
          (
              labels_chunk,
              centers_new_chunk,
              weight_in_clusters_chunk,
          ) = chunk_op.new_chunks(
              [
                  x_chunk,
                  sample_weight_chunk,
                  x_squared_norms_chunk,
                  op.centers_old.chunks[0],
                  labels_chunk,
              ],
              kws=chunk_kws,
          )
          labels_chunks.append(labels_chunk)
          centers_new_chunks.append(centers_new_chunk)
          weight_in_clusters_chunks.append(weight_in_clusters_chunk)
  
      if op.update_centers:
          # merge centers_new and weight_in_clusters
          merge_op = KMeansLloydUpdate(
              stage=OperandStage.reduce, n_clusters=op.n_clusters
          )
          merge_chunk_kw = [
              centers_new_chunks[0].params,
              weight_in_clusters_chunks[0].params,
          ]
          (
              centers_new_chunk,
              weight_in_cluster_chunk,
          ) = merge_op.new_chunks(
              centers_new_chunks + weight_in_clusters_chunks,
              kws=merge_chunk_kw,
          )
      else:
          # the data is meaningless, just pick one
          centers_new_chunk = centers_new_chunks[0]
          weight_in_cluster_chunk = weight_in_clusters_chunks[0]
  
      out_params = [out.params for out in op.outputs]
      # labels
      out_params[0]["nsplits"] = labels.nsplits
      out_params[0]["chunks"] = labels_chunks
      # centers_new
      out_params[1]["nsplits"] = tuple(
          (s,) for s in op.outputs[1].shape
      )
      out_params[1]["chunks"] = [centers_new_chunk]
      # weight_in_clusters
      out_params[2]["nsplits"] = tuple(
          (s,) for s in op.outputs[2].shape
      )
      out_params[2]["chunks"] = [weight_in_cluster_chunk]
      new_op = op.copy()
      return new_op.new_tileables(op.inputs, kws=out_params)
  
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 13 for calling the KMeansLloydUpdate obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op