code: def tile(cls, op: "XGBTrain"):
      ctx = get_context()
  
      inp = op.inputs[0]
      in_chunks = inp.chunks
      workers = cls._get_dmatrix_chunks_workers(ctx, inp)
      worker_to_in_chunks = dict(zip(workers, in_chunks))
      n_chunk = len(in_chunks)
      out_chunks = []
      worker_to_evals = defaultdict(dict)
      if op.evals is not None:
          for dm, ev in op.evals:
              ev_workers = cls._get_dmatrix_chunks_workers(ctx, dm)
              for ev_worker, ev_chunk in zip(ev_workers, dm.chunks):
                  worker_to_evals[ev_worker][ev] = ev_chunk
  
      all_workers = set(workers)
      all_workers.update(worker_to_evals)
  
      i = itertools.count(n_chunk)
      tracker_chunk = StartTracker(
          n_workers=len(all_workers), pure_depends=[True] * n_chunk
      ).new_chunk(in_chunks, shape=())
      for worker in all_workers:
          chunk_op = op.copy().reset_key()
          chunk_op.expect_worker = worker
          chunk_op.tracker = tracker_chunk
          if worker in worker_to_in_chunks:
              in_chunk = worker_to_in_chunks[worker]
          else:
              in_chunk_op = ToDMatrix(
                  data=None,
                  label=None,
                  weight=None,
                  base_margin=None,
                  missing=inp.op.missing,
                  feature_names=inp.op.feature_names,
                  feature_types=inp.op.feature_types,
                  _output_types=inp.op.output_types,
              )
              params = inp.params.copy()
              params["index"] = (next(i), 0)
              params["shape"] = (0, inp.shape[1])
              in_chunk = in_chunk_op.new_chunk(None, kws=[params])
          chunk_evals = []
          for dm, ev in op.evals:
              try:
                  chunk_evals.append((worker_to_evals[worker][ev], ev))
              except KeyError:
                  # create a new eval chunk
                  eval_chunk_op = ToDMatrix(
                      data=None,
                      label=None,
                      weight=None,
                      base_margin=None,
                      missing=dm.op.missing,
                      feature_names=dm.op.feature_names,
                      feature_types=dm.op.feature_types,
                      _output_types=dm.op.output_types,
                  )
                  params = dm.params.copy()
                  params["index"] = (0, 0)
                  params["shape"] = (0, dm.shape[1])
                  eval_chunk = eval_chunk_op.new_chunk(
                      None, kws=[params]
                  )
                  chunk_evals.append((eval_chunk, ev))
          chunk_op.evals = chunk_evals
          input_chunks = (
              [in_chunk]
              + [pair[0] for pair in chunk_evals]
              + [tracker_chunk]
          )
          out_chunk = chunk_op.new_chunk(
              input_chunks, shape=(np.nan,), index=in_chunk.index[:1]
          )
          out_chunks.append(out_chunk)
  
      new_op = op.copy()
      return new_op.new_tileables(
          op.inputs,
          chunks=out_chunks,
          nsplits=((np.nan for _ in out_chunks),),
      )
  
imports: import itertools
  import logging
  import pickle
  from collections import OrderedDict, defaultdict
  import numpy
  from ....None import opcodes
  from ....core import OutputType
  from ....core.context import get_context
  from ....core.operand import MergeDictOperand
  from ....serialization.serializables import FieldTypes, DictField, KeyField, ListField
  from ....utils import ensure_own_data
  from .dmatrix import ToDMatrix, to_dmatrix
  from .start_tracker import StartTracker
label_desc: Use image node_id 5 for calling the XGBTrain obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op