class_docstr: It is a rolling based on DDG-DA
  
  **NOTE**
  before running the example, please clean your previous results with following command
  - `rm -r mlruns`
class_docstr_tok: ["It","is","a","rolling","based","on","DDG-DA","*","*","NOTE","*","*","before","running","the","example",",","please","clean","your","previous","results","with","following","command","-","`","rm","-r","mlruns","`"]
docstr: Dump data for training meta model.
  This function will dump the input data for meta model
docstr_tok: ["Dump","data","for","training","meta","model",".","This","function","will","dump","the","input","data","for","meta","model"]
code: def _dump_meta_ipt(self):
      """
      Dump data for training meta model.
      This function will dump the input data for meta model
      """
      # According to the experiments, the choice of the model type is very important for achieving good results
      sim_task = self._adjust_task(
          self.basic_task(enable_handler_cache=False),
          astype=self.sim_task_model,
      )
      sim_task = replace_task_handler_with_cache(
          sim_task, self.working_dir
      )
  
      if self.sim_task_model == "gbdt":
          sim_task["model"].setdefault("kwargs", {}).update(
              {"early_stopping_rounds": None, "num_boost_round": 150}
          )
  
      exp_name_sim = f"data_sim_s{self.step}"
  
      internal_data = InternalData(
          sim_task, self.step, exp_name=exp_name_sim
      )
      internal_data.setup(trainer=TrainerR)
  
      with self._internal_data_path.open("wb") as f:
          pickle.dump(internal_data, f)
  
imports: from pathlib import Path
  import pickle
  from typing import Optional, Union
  import pandas
  import yaml
  from qlib.contrib.meta.data_selection.dataset import InternalData, MetaDatasetDS
  from qlib.contrib.meta.data_selection.model import MetaModelDS
  from qlib.data.dataset.handler import DataHandlerLP
  from qlib.model.meta.task import MetaTask
  from qlib.model.trainer import TrainerR
  from qlib.typehint import Literal
  from qlib.utils import init_instance_by_config
  from qlib.workflow import R
  from qlib.workflow.task.utils import replace_task_handler_with_cache
  from .base import Rolling
label_desc: Use image node_id 6 for calling the DDGDA obj's underlying member method code with example usage: obj._dump_meta_ipt() without return types