class_docstr: A delayed implementation based on TrainerRM, which means `train` method may only do some preparation and `end_train` method can do the real model fitting.
class_docstr_tok: ["A","delayed","implementation","based","on","TrainerRM",",","which","means","`","train","`","method","may","only","do","some","preparation","and","`","end_train","`","method","can","do","the","real","model","fitting","."]
code: def has_worker(self) -> bool:
      return True
  
imports: import socket
  from typing import Callable, List, Optional
  from tqdm.auto import tqdm
  from qlib.config import C
  from qlib.data.dataset import Dataset
  from qlib.data.dataset.weight import Reweighter
  from qlib.log import get_module_logger
  from qlib.model.base import Model
  from qlib.utils import auto_filter_kwargs, fill_placeholder, flatten_dict, init_instance_by_config
  from qlib.utils.paral import call_in_subproc
  from qlib.workflow import R
  from qlib.workflow.recorder import Recorder
  from qlib.workflow.task.manage import TaskManager, run_task
label_desc: Use image node_id 5 for calling the DelayTrainerRM obj's underlying member method code with example usage: obj.has_worker() and returns: True