class_docstr: Trainer based on (R)ecorder and Task(M)anager.
  It can train a list of tasks and return a list of model recorders in a multiprocessing way.
  
  Assumption: `task` will be saved to TaskManager and `task` will be fetched and trained from TaskManager
class_docstr_tok: ["Trainer","based","on","(","R",")","ecorder","and","Task","(","M",")","anager",".","It","can","train","a","list","of","tasks","and","return","a","list","of","model","recorders","in","a","multiprocessing","way",".","Assumption",":","`","task","`","will","be","saved","to","TaskManager","and","`","task","`","will","be","fetched","and","trained","from","TaskManager"]
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 TrainerRM obj's underlying member method code with example usage: obj.has_worker() and returns: True