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","`"]
code: def run(self):
      # prepare the meta model for rolling ---------
      # 1) file: handler_proxy.pkl (self.proxy_hd)
      self._dump_data_for_proxy_model()
      # 2)
      # file: internal_data_s20.pkl
      # mlflow: data_sim_s20, models for calculating meta_ipt
      self._dump_meta_ipt()
      # 3) meta model will be stored in `DDG-DA`
      self._train_meta_model()
  
      # Run rolling --------------------------------
      # 4) new_tasks are saved in "tasks_s20.pkl" (reweighter is added)
      # - the meta inference are done when calling `get_task_list`
      # 5) load the saved tasks and train model
      super().run()
  
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 10 for calling the DDGDA obj's underlying member method code with example usage: obj.run() without return types