code: def execute(cls, ctx, op: "XGBTrain"):
      if op.merge:
          return super().execute(ctx, op)
  
      from xgboost import train, rabit
  
      params = op.params.copy()
  
      n_threads = 0
      if op.tracker is None:
          # non distributed
          ctx_n_threads = -1
      else:
          # distributed
          ctx_n_threads = ctx.get_slots()
  
      # fix parallelism on nodes
      for p in ["nthread", "n_jobs"]:
          if (
              params.get(p, None) is not None
              and params.get(p, ctx_n_threads) != ctx_n_threads
          ):  # pragma: no cover
              logger.info(
                  "Overriding `nthreads` defined in Mars worker."
              )
              n_threads = params[p]
              break
      if n_threads == 0 or n_threads is None:  # pragma: no branch
          n_threads = ctx_n_threads
      params.update({"nthread": n_threads, "n_jobs": n_threads})
  
      dtrain = ToDMatrix.get_xgb_dmatrix(
          ensure_own_data(ctx[op.dtrain.key]), nthread=n_threads
      )
      evals = tuple()
      if op.evals is not None:
          eval_dmatrices = [
              ToDMatrix.get_xgb_dmatrix(
                  ensure_own_data(ctx[t[0].key]), nthread=n_threads
              )
              for t in op.evals
          ]
          evals = tuple(
              (m, ev[1]) for m, ev in zip(eval_dmatrices, op.evals)
          )
  
      if op.tracker is None:
          # non distributed
          local_history = dict()
          kwargs = dict() if op.kwargs is None else op.kwargs
          bst = train(
              params,
              dtrain,
              evals=evals,
              evals_result=local_history,
              **kwargs,
          )
          ctx[op.outputs[0].key] = {
              "booster": pickle.dumps(bst),
              "history": local_history,
          }
      else:
          # distributed
          logger.debug("Distributed train params: %r", params)
  
          rabit_args = ctx[op.tracker.key]
          rabit.init(
              [
                  arg.tobytes() if isinstance(arg, memoryview) else arg
                  for arg in rabit_args
              ]
          )
          try:
              logger.debug(
                  "Start to train data, train size: %s, evals sizes: %s",
                  dtrain.num_row(),
                  [ev[0].num_row() for ev in evals],
              )
              local_history = dict()
              bst = train(
                  params,
                  dtrain,
                  evals=evals,
                  evals_result=local_history,
                  **op.kwargs,
              )
              ret = {
                  "booster": pickle.dumps(bst),
                  "history": local_history,
              }
              if rabit.get_rank() != 0:
                  ret = {}
              ctx[op.outputs[0].key] = ret
          finally:
              rabit.finalize()
  
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 6 for calling the XGBTrain obj's underlying member method code with example usage: obj.execute(cls, ctx, op) and returns: super