code: def tile(cls, op: "MarsDMatrix"):
      data, label, weight, base_margin = (
          op.data,
          op.label,
          op.weight,
          op.base_margin,
      )
  
      if has_unknown_shape(data):
          yield
      if data.chunk_shape[1] > 1:
          # make sure data's second dimension has only 1 chunk
          data = yield from recursive_tile(
              data.rechunk({1: data.shape[1]})
          )
      nsplit = data.nsplits[0]
      # rechunk label
      if label is not None:
          label = yield from recursive_tile(label.rechunk({0: nsplit}))
      # rechunk weight
      if weight is not None:
          weight = yield from recursive_tile(
              weight.rechunk({0: nsplit})
          )
      # rechunk base_margin
      if base_margin is not None:
          base_margin = yield from recursive_tile(
              base_margin.rechunk({0: nsplit})
          )
  
      collocated = cls._get_collocated(
          op, data, label, weight, base_margin
      )
      collocated_chunks = list(
          itertools.chain.from_iterable(c.chunks for c in collocated)
      )
      yield collocated_chunks + collocated
  
      data = build_fetch(collocated[0])
      has_label = False
      if label is not None:
          has_label = True
          label = build_fetch(collocated[1])
      i_weight = -1
      if weight is not None:
          i_weight = 1 if not has_label else 2
          weight = build_fetch(collocated[i_weight])
      if base_margin is not None:
          base_margin = build_fetch(collocated[-1])
  
      ctx = get_context()
  
      # for distributed, we should concat the chunks
      # which allocated on the same worker into one
      data_chunk_metas = ctx.get_chunks_meta(
          [c.key for c in data.chunks], fields=["bands"]
      )
      data_chunk_workers = [m["bands"][0][0] for m in data_chunk_metas]
      worker_to_chunks = dict()
      for i, worker in enumerate(data_chunk_workers):
          size = 1 + sum(
              it is not None for it in [label, weight, base_margin]
          )
          if worker not in worker_to_chunks:
              worker_to_chunks[worker] = [[] for _ in range(size)]
          worker_to_chunks[worker][0].append(data.chunks[i])
          if label is not None:
              worker_to_chunks[worker][1].append(label.chunks[i])
          if weight is not None:
              worker_to_chunks[worker][i_weight].append(
                  weight.chunks[i]
              )
          if base_margin is not None:
              worker_to_chunks[worker][-1].append(base_margin.chunks[i])
      ind = itertools.count(0)
      out_chunks = []
      for worker, chunks in worker_to_chunks.items():
          data_chunk = concat_chunks(cls._order_chunk_index(chunks[0]))
          inps = [data_chunk]
          label_chunk = None
          if label is not None:
              label_chunk = concat_chunks(
                  cls._order_chunk_index(chunks[1])
              )
              inps.append(label_chunk)
          weight_chunk = None
          if weight is not None:
              weight_chunk = concat_chunks(
                  cls._order_chunk_index(chunks[i_weight])
              )
              inps.append(weight_chunk)
          base_margin_chunk = None
          if base_margin is not None:
              base_margin_chunk = concat_chunks(
                  cls._order_chunk_index(chunks[-1])
              )
              inps.append(base_margin_chunk)
          chunk_op = ToDMatrix(
              data=data_chunk,
              label=label_chunk,
              missing=op.missing,
              weight=weight_chunk,
              base_margin=base_margin_chunk,
              feature_names=op.feature_names,
              feature_types=op.feature_types,
              _output_types=op.output_types,
          )
          kws = data_chunk.params
          kws["index"] = (next(ind), 0)
          out_chunks.append(chunk_op.new_chunk(inps, **kws))
      nsplits = (
          tuple(c.shape[0] for c in out_chunks),
          (out_chunks[0].shape[1],),
      )
  
      new_op = op.copy()
      kw = op.outputs[0].params
      kw["chunks"] = out_chunks
      kw["nsplits"] = nsplits
      return new_op.new_tileables(op.inputs, kws=[kw])
  
imports: import itertools
  from typing import List, Union
  import numpy
  from ....None import opcodes
  from ....core import get_output_types, recursive_tile
  from ....core.context import get_context, Context
  from ....dataframe.core import DATAFRAME_TYPE
  from ....serialization.serializables import KeyField, Float64Field, ListField, BoolField
  from ....tensor.core import TENSOR_TYPE, TENSOR_CHUNK_TYPE
  from ....tensor import tensor
  from ....typing import TileableType, ChunkType
  from ....utils import has_unknown_shape, ensure_own_data, build_fetch
  from ...operands import LearnOperand, LearnOperandMixin
  from ...utils import convert_to_tensor_or_dataframe, concat_chunks
label_desc: Use image node_id 7 for calling the ToDMatrix obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op