code: def tile(cls, op: "KMeansElkanUpdate"):
      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]})
      )
      labels = yield from recursive_tile(
          op.labels.rechunk({0: x.nsplits[0]})
      )
      upper_bounds = yield from recursive_tile(
          op.upper_bounds.rechunk({0: x.nsplits[0]})
      )
      lower_bounds = yield from recursive_tile(
          op.lower_bounds.rechunk(
              {0: x.nsplits[0], 1: op.lower_bounds.shape[1]}
          )
      )
      centers_old = yield from recursive_tile(
          op.centers_old.rechunk(op.centers_old.shape)
      )
      center_half_distances = yield from recursive_tile(
          op.center_half_distances.rechunk(
              op.center_half_distances.shape
          )
      )
      distance_next_center = yield from recursive_tile(
          op.distance_next_center.rechunk(op.distance_next_center.shape)
      )
  
      out_chunks = [list() for _ in range(op.output_limit)]
      for i in range(x.chunk_shape[0]):
          x_chunk = x.cix[i, 0]
          sample_weight_chunk = sample_weight.cix[i,]
          labels_chunk = labels.cix[i,]
          upper_bounds_chunk = upper_bounds.cix[i,]
          lower_bounds_chunk = lower_bounds.cix[i, 0]
          chunk_op = op.copy().reset_key()
          chunk_op.stage = OperandStage.map
          chunk_kws = list(
              (
                  # labels
                  labels_chunk.params,
                  # upper_bounds
                  upper_bounds_chunk.params,
                  # lower_boudns
                  lower_bounds_chunk.params,
              )
          )
          # centers_new
          chunk_kws.append(
              {
                  "index": (0, 0),
                  "shape": (op.n_clusters, x_chunk.shape[1]),
                  "dtype": centers_old.dtype,
                  "order": TensorOrder.C_ORDER,
              }
          )
          # weight_in_clusters
          chunk_kws.append(
              {
                  "index": (0,),
                  "shape": (op.n_clusters,),
                  "dtype": centers_old.dtype,
                  "order": TensorOrder.C_ORDER,
              }
          )
          chunks = chunk_op.new_chunks(
              [
                  x_chunk,
                  sample_weight_chunk,
                  centers_old.chunks[0],
                  center_half_distances.chunks[0],
                  distance_next_center.chunks[0],
                  labels_chunk,
                  upper_bounds_chunk,
                  lower_bounds_chunk,
              ],
              kws=chunk_kws,
          )
          assert len(chunks) == len(out_chunks)
          for oc, c in zip(out_chunks, chunks):
              oc.append(c)
  
      (
          label_chunks,
          upper_bounds_chunks,
          lower_bounds_chunks,
      ) = out_chunks[:3]
      centers_new_chunks, weight_in_cluster_chunks = out_chunks[3:]
  
      if op.update_centers:
          # merge centers_new and weight_in_clusters
          merge_op = KMeansElkanUpdate(
              stage=OperandStage.reduce, n_clusters=op.n_clusters
          )
          merge_chunk_kw = [
              centers_new_chunks[0].params,
              weight_in_cluster_chunks[0].params,
          ]
          (
              centers_new_chunk,
              weight_in_cluster_chunk,
          ) = merge_op.new_chunks(
              centers_new_chunks + weight_in_cluster_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_cluster_chunks[0]
  
      out_params = [out.params for out in op.outputs]
      # labels
      out_params[0]["nsplits"] = labels.nsplits
      out_params[0]["chunks"] = label_chunks
      # upper_bounds
      out_params[1]["nsplits"] = upper_bounds.nsplits
      out_params[1]["chunks"] = upper_bounds_chunks
      # lower_bounds
      out_params[2]["nsplits"] = lower_bounds.nsplits
      out_params[2]["chunks"] = lower_bounds_chunks
      # centers_new
      out_params[3]["nsplits"] = tuple(
          (s,) for s in op.outputs[3].shape
      )
      out_params[3]["chunks"] = [centers_new_chunk]
      # weight_in_clusters
      out_params[4]["nsplits"] = tuple(
          (s,) for s in op.outputs[4].shape
      )
      out_params[4]["chunks"] = [weight_in_cluster_chunk]
      new_op = op.copy()
      return new_op.new_tileables(op.inputs, kws=out_params)
  
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 16 for calling the KMeansElkanUpdate obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op