code: def tile(cls, op: "KMeansElkanInitBounds"):
      # unify chunks on axis 0
      if has_unknown_shape(op.centers, op.center_half_distances):
          yield
      x = op.x
      centers = yield from recursive_tile(
          op.centers.rechunk(op.centers.shape)
      )
      center_half_distances = yield from recursive_tile(
          op.center_half_distances.rechunk(
              op.center_half_distances.shape
          )
      )
  
      out_chunks = [list() for _ in range(op.output_limit)]
      for c in x.chunks:
          chunk_op = op.copy().reset_key()
          chunk_params = []
          # labels chunk
          chunk_params.append(
              {
                  "shape": (c.shape[0],),
                  "index": (c.index[0],),
                  "dtype": np.dtype(np.int32),
                  "order": TensorOrder.C_ORDER,
              }
          )
          # upper bounds
          chunk_params.append(
              {
                  "shape": (c.shape[0],),
                  "index": (c.index[0],),
                  "dtype": c.dtype,
                  "order": TensorOrder.C_ORDER,
              }
          )
          # lower bounds
          chunk_params.append(
              {
                  "shape": (c.shape[0], op.n_clusters),
                  "index": (c.index[0], 0),
                  "dtype": c.dtype,
                  "order": TensorOrder.C_ORDER,
              }
          )
          chunks = chunk_op.new_chunks(
              [c, centers.chunks[0], center_half_distances.chunks[0]],
              kws=chunk_params,
          )
          for i, out_chunk in enumerate(chunks):
              out_chunks[i].append(out_chunk)
  
      out_nsplits = [
          (x.nsplits[0],),
          (x.nsplits[0],),
          (x.nsplits[0], (op.n_clusters,)),
      ]
      out_params = [out.params for out in op.outputs]
      for i, chunks in enumerate(out_chunks):
          out_params[i]["chunks"] = chunks
      for i, nsplits in enumerate(out_nsplits):
          out_params[i]["nsplits"] = nsplits
      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 9 for calling the KMeansElkanInitBounds obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op